Tmux is a tool Thomas Bustos calls the backbone of his productivity. In this episode, he walks through his full tech stack, explains how deep work sessions are structured, and shows how Tmux integrates with NeoVim and Cloud Code for maximum efficiency.

Learn how to track metrics, iterate on user experience, and keep your workflow aligned with your goals. 

If you want to move from “busy” to “productive,” this episode is a must-listen.

Top Takeaways:

• Cloud tools can enhance productivity but aren't the sole solution.

• Tmux is a core tool for managing sessions effectively.

• Daily skills help manage entropy and keep goals aligned.

• Using a tech stack that includes NeoVim and Cloud Code is beneficial.

• Iterating on user experience is crucial for app development.

• Context management is key in software engineering.

• Open source tools can be integrated for better workflow.

• Security and setup considerations are important for cloud tools.

• Deep work sessions can lead to more productive outcomes.

• Feedback and iteration are essential in the design process.

Chapters:

• 00:00 Welcome!

• 02:47 Exploring the Tech Stack

• 06:07 Deep Work and Project Management

• 08:55 Iterating on User Experience

• 12:12 Final Thoughts on Engineering Tools

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Not long ago, being an “AI leader” meant hiring a few ML engineers, experimenting with models, and hoping something stuck. Today, that approach fails. AI leaders are no longer just technical champions. They are system architects of decision-making, accountability, and value creation.

In this episode of Let’s Talk AI, Thomas Bustos breaks down the three pillars every AI leader must master to build real, measurable impact inside an organization. He explains why teaching is no longer optional, why strategy without execution collapses, and why implementation is where most AI initiatives die.

If you’re serious about becoming an AI leader, or building AI leadership inside your company, this episode gives you the blueprint.

Listen now.

Top Takeaways:

• The core goal is to balance leading and delivering technology.

• AI leaders must teach, strategize, and implement effectively.

• Successful AI adoption can compound gains for organizations.

• Metrics like error rates and active users are crucial for success.

• Every build should enhance observability for future improvements.

• AI leaders need to understand the latest tools and their applications.

• Reverting to previous versions is essential for error handling.

• Quantifying AI's impact in terms of revenue is recommended.

• A learning organization adapts and grows through shared knowledge.

• Effective implementation requires speed, reliability, and quality.

Chapters:

• 00:00 Welcome!

• 02:50 The AI Leaders Playbook: Key Pillars

• 06:03 Understanding AI Adoption and Its Impact

• 09:10 Strategies for Effective Implementation

• 11:58 Metrics for AI Success

• 14:51 Navigating AI Tools and Technologies

• 18:07 Building a Learning Organization

• 20:59 Final Thoughts

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

It’s not someone who occasionally uses ChatGPT.

It’s not someone who automates a few workflows.

It’s someone who integrates AI into how they think, decide, and operate.

In this episode, Thomas Bustos explores the rise of the AI Native employee—not as a job title, but as a new operating standard. Before AI, employees manually summarized, synthesized, reported, and shared knowledge. Decision-making required slow coordination. Context was fragmented.

Now, the AI Native shift is changing how organizations think, decide, and execute.

The AI Native employee defines what great looks like.

They set constraints.

They design systems.

They use AI to enhance clarity, not to outsource thinking.

Thomas Bustos breaks down why companies that fail to build AI Native systems will struggle with accountability, context gaps, and slow decision loops. And why the real competitive advantage is how employees integrate AI into learning velocity and decision quality.

This episode is a blueprint for leaders who want to move from AI curiosity to AI Native execution.

Top Takeaways:

• If your company is just getting seats for people to ask things to ChatGPT, you definitely need to change something.

• Create accountability and a motion of learning velocity.

• The more connected your context system is, the better decisions can be made.

• The quality of decisions depends on how much reality your team can see.

• Software is going to zero, meaning the cost of building solutions is decreasing.

• The jobs are not going anywhere; they are evolving with technology.

• If you can generate more impact, you are more valuable.

• Creating systems that enhance learning and context is crucial for growth.

• This is why we're playing this game: to continuously learn and adapt.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos analyzes how AI native companies restructure three critical dimensions: learning velocity, decision quality, and organizational leverage. The discussion outlines how teams can use AI to improve strategic clarity, accelerate feedback loops, and create shared maps of reality across product, sales, and leadership.

The episode also examines how AI native companies change internal dynamics—shifting from siloed expertise toward collaborative intelligence systems where humans and AI co-create insights. Thomas breaks down the implications for early-stage growth, team empowerment, and the future of work, highlighting why startups that fail to evolve into AI native companies will struggle to compete with organizations built natively for AI-enabled decision-making.

This framework positions AI not as a productivity hack, but as an organizational multiplier.

Top Takeaways:

• The better your questions, the more valuable you are as the founder.

• AI native companies focus on culture and systems, not just tools.

• You need blueprints and processes to exceed early revenue stages.

• Context lives in people's heads and dies when they leave the room.

• AI amplifies opinions and requires strong leadership to be effective.

• No human in the loop means empowering humans to make better decisions.

• Building a context engine can improve decision-making and growth.

• AI native companies have a unique competitive advantage over larger firms.

• Understanding core concepts is essential for strategic growth.

• Evaluating AI nativeness helps identify areas for improvement.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos challenges the obsession with growth, optimization, and “best practices.” He argues that for an early stage founder, those things are distractions without a learning engine underneath them.

Instead of asking “How do we grow faster?”, the better question is “How do we learn faster?” This episode explains why premature scaling breaks teams, why metrics without context mislead founders, and why decision quality compounds more than growth ever will.

If you’re an early stage founder feeling pressure to move faster without clarity, this episode is a necessary counterweight.

Top Takeaways:

• The journey to scaling requires a clear understanding of what to do and what to stop doing.

• Early stage founders must focus on building a strong operating system to navigate growth.

• A learning engine is crucial for making informed decisions and improving outcomes.

• Resource allocation should be strategic, focusing on the best bets for growth.

• Aligning sales and product development is key to delivering value to customers.

• Understanding the ideal customer profile helps in refining business strategies.

• Pattern identification can lead to better decision-making and revenue generation.

• Founders should embrace chaos as a learning opportunity to refine their approach.

• A methodical path reduces uncertainty and enhances clarity in decision-making.

• The ability to adapt and iterate is essential for early stage success.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos challenges the default startup narrative and argues that early stage founders should delay optimization, ignore premature scale, and focus obsessively on their first five customers. Not leads. Not users. Paying, real ICP customers.

He dismantles the obsession with metrics that don’t matter yet and explains why clarity, learning velocity, and decision quality outperform hustle and headcount in the early days. The discussion explores why premature scaling leads to fragile systems, how visibility enables better decisions, and why early-stage teams must prioritize shared context over efficiency. 

Thomas also explains how tracking inputs and outputs creates feedback loops that prevent founders from optimizing the wrong things.

This episode serves as a systems-level analysis of what actually matters for early stage founders before scale, funding, or hiring accelerates complexity.

Top Takeaways:

• Early stage founders should optimize for learning over growth.

• Focus on inputs before outputs to ensure effective decision-making.

• Create a shared map of reality to align the team.

• User feedback is crucial for refining the mission and ideal customer profile.

• Metrics should reflect reality, not be treated as goals.

• High signal users provide valuable insights for progress.

• Building foundations is essential before scaling a startup.

• Learning velocity and decision quality are key to success.

• The early stage is characterized by messiness and uncertainty.

• Navigating through a dynamic map is vital for achieving product-market fit.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

What starts as a leap from a stable career becomes a story about obsession, discipline, and the uncomfortable truths behind real startup growth. Max shares the moment he realized community was the key to expansion. He talks about the nights spent rethinking distribution, the emotional rollercoaster of working with both technical and non-technical co-founders, and the mindset that transformed Lucis from an idea into a fast-emerging longevity platform.

This is a story of a founder recounting the lessons that scarred him, shaped him, and ultimately strengthened his ability to scale a startup in one of the toughest markets.

If you want startup growth lessons that go beyond clichés, this is the episode to hear.

Top Takeaways:

• Max is the founder of Lucis, focusing on health and longevity.

• Building a community around health can drive engagement and growth.

• Distribution should be prioritized from day one in product development.

• It's important to start with an idea and iterate rather than waiting for perfection.

• Y Combinator provided invaluable lessons and a life-changing experience for Max and his co-founders.

• Relentlessness is key to building a successful startup.

• Continuous learning is essential for founders as they navigate different stages of growth.

• The mission of Lucis is to make preventative health accessible in Europe.

• Max's personal experience with health data drives the mission of Lucis

Connect with Max Berthelot

• Max Berthelot on LinkedIn - https://www.linkedin.com/in/maximeberthelot/

• Instagram: @lucislife_en   

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos and Richard Wang (Clad Labs) dissect the architectural and strategic implications of building AI-powered IDEs. They discuss how real-time feedback loops, adaptive code assistance, and contextual learning models are reshaping both the developer experience and the business of software.

This is a must-listen for engineers, founders, and product builders who want to understand the competitive edge behind AI-driven development tools, and why IDE innovation is the next major battleground for developer productivity.

Top Insights:

• AI is revolutionizing the coding process for developers.

• Integrating ads into IDEs can subsidize development costs.

• User feedback is crucial for product iteration and improvement.

• The future of coding involves AI-native tools that enhance productivity.

• Democratizing access to AI tools is essential for global developers.

• Speed of development should focus on user-centric design.

• Understanding user workflows is key to building effective tools.

• The competitive landscape of IDEs is evolving with new approaches.

• Maintaining a balance between speed and quality is vital in development.

• The mission is to unlock coding opportunities for everyone.

Connect with Richard Wang

• Richard Wang on LinkedIn - https://www.linkedin.com/in/richard-wang-39554994/

• Clad Labs AI - https://www.cladlabs.ai/ 

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Anmol Sood, former Meta engineer and now founder of Alai, opens up about his journey from writing production-grade code to creating a startup that helps people build high-quality presentations in minutes.

He shares how speed and quality constantly compete for attention in early-stage product development, why co-founder dynamics can make or break momentum, and the ultimate secret to building products that users love.

For anyone navigating the startup ecosystem, Anmol’s story offers a grounded view of what it means to build, iterate, and keep your vision alive amid uncertainty.

🎧 Listen to the full episode and discover how startup success is built one decision at a time.

Top Insights:

• Anmol emphasizes the importance of working on problems that matter to you.

• Building skills is not just about technical ability but also about caring for the problem.

• Transitioning from a large company to a startup requires unlearning certain practices.

• Identifying a clear target audience is crucial for product success.

• Finding early partners can provide valuable feedback and insights.

• Intuition plays a key role in product development, especially pre-product market fit.

• Understanding customer incentives is essential for effective partnerships.

• Design quality is a significant differentiator in presentation tools.

• Balancing speed and quality is vital in the development process.

• Strong co-founder relationships are foundational to startup success.

Connect with Anmol Sood

• Anmol Sood on LinkedIn - https://www.linkedin.com/in/anmolsood/

• Anmol Sood on X - https://x.com/anmolsood21 

• Alai - https://getalai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode of Let’s Talk AI, Thomas Bustos sits down with Remi Fabre, a robotics engineer whose work bridges the gap between software intelligence and physical motion. Together, they unpack what it takes to design robots that don’t just move, but adapt, learn, and connect with the world around them.

From the elegance of inverse kinematics to the power of open-source collaboration, Rémi shares how robotics has evolved from mechanical systems into intelligent ecosystems. They also explore the emotional dimension of robotics and why people form bonds with machines, and how AI is amplifying that connection.

If you’ve ever wondered how intelligence takes shape in the real world, this episode reveals what’s next at the intersection of AI and robotics, where the digital brain meets the physical body.

Top Insights:

• It's important for the human brain to do stuff that is hard.

• Open source has perks from the marketing side.

• You need to be comfortable with failure and iteration.

• The key word of robotics is robust.

• AI has access only to the high-level decision for now.

• I think it's fine that the robot has a personality.

• You can create a new language for movement.

• The future of robotics is incredibly exciting.

• You should always try to improve on what you're doing.

Connect with Remi Fabre

• Remi Fabre on LinkedIn - https://fr.linkedin.com/in/remifabre

• Remi Fabre on YouTube - https://www.youtube.com/channel/UCDuIkx0YEzUMgzpVUu3aatw 

• Remi Fabre on X - https://x.com/RemiFabreRobot

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos dissects the AI Engineer Playbook, a systems-level look at how great builders think, learn, and execute.

He unpacks how to design your own playbook, why copying others will never scale, and how the future belongs to engineers who combine speed, control, and empathy for users.

You’ll walk away challenged to rethink your process, reconnect with your community, and commit to growth that’s as intentional as it is technical.

Listen to this solo deep dive into what it truly takes to create, learn, and lead as an AI engineer.

Top Insights:

• Understand the value of solving problems and time saved.

• Minimize human involvement in engineering processes.

• Focus on user experience to solve the right problems.

• Building is a long-term game requiring curiosity and iteration.

• Define your unique playbook based on personal interests.

• Speed and control are essential in engineering excellence.

• Engage with communities and share your work publicly.

• Mentorship is crucial for growth and direction.

• Iterate on your goals weekly for continuous improvement.

• Be curious and explore new tools and technologies.

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Adam Haney of Infinity Constellation breaks down how modern teams can achieve product-market fit while preserving a human-centric mindset. The discussion examines the convergence of AI tools, leadership communication, and strategic hiring, offering a lens into how effective collaboration fuels innovation. 

It’s a grounded conversation on what tech leadership looks like in 2025 and beyond.

Top Insights:

• Technology must harmonize with human-centric services.

• The zero to one phase is crucial for startups.

• AI tools are changing the landscape of product development.

• Unified vision enhances team velocity and effectiveness.

• Building relationships between tech and operations is vital.

• Iterative frameworks help in strategic planning.

• Experience teaches the importance of adaptability in leadership. The best AI services businesses need to be tightly coupled with operations.

• Hands-on experience in engineering fosters a well-rounded skill set.

• AI services are becoming viable for hyperscale companies.

• Understanding customer needs is essential for service success.

• Intuition and data should reinforce each other in decision-making.

• Surrounding yourself with ambitious people raises your own standards.

Connect with Adam Haney

• Adam Haney on LinkedIn - https://www.linkedin.com/in/adamjhaney/ 

• Infinity Constellation - https://www.infinityconstellation.com/ 

• Open Story Podcast - https://openstorypodcast.com/ 

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

While most platforms focus on distribution, he’s betting on AI as the core experience. As co-founder of Snipd, Kevin argues that the future of podcasting isn’t about more shows, but about smarter listening: capturing key ideas, surfacing highlights, and creating shareable knowledge from endless hours of audio. 

In this conversation, Thomas Bustos pushes on the big questions: is AI enhancing the podcast experience, or is it reshaping it entirely? What happens when AI curates your takeaways better than you can? 

For anyone who thinks AI is just a background tool, this episode is a wake-up call. The podcast player itself is becoming intelligent.

Top Insights:

• Decide what to focus on and forget everything else.

• Podcasts are the largest knowledge library in the world.

• You have to be relentlessly resourceful as a founder.

• Talk to people to understand their problems before building.

• Give it to five people, and you'll see the problems immediately.

• Snipd is a fully fledged podcast player with unique features.

• Put small building blocks on top of each other for success.

• The world needs more entrepreneurial energy and spirit.

• AI is changing how we interact with audio content.

Exclusive Listener Offer!

One month free premium access on Snipd - https://link.snipd.com/Cx7S/thomas 

Connect with Kevin Smith

• Kevin Smith on LinkedIn - https://ch.linkedin.com/in/kevin-smith-673714b4 

• Snipd - https://www.snipd.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode of Let’s Talk AI, we explore why the future of AI is no longer just about tools. It’s now about people who can think critically, solve problems, and apply frameworks with purpose.

Alessandro breaks down the evolution of roles in data science, the rise of hands-on workshops for real-world learning, and the practical use of frameworks like Crew AI and LangGraph.

He also explains why observability isn’t a buzzword but a necessity for responsible AI development.

If you’ve ever wondered how AI engineers can deliver business value without drowning in hype, this episode offers a grounded, professional perspective.

Top Insights:

• A professional perspective in data science requires domain specificity.

• Experiences at conferences can lead to valuable networking opportunities.

• The role of a data scientist is evolving and often overlaps with AI engineering.

• AI engineering is about building solutions, not just maintaining infrastructure.

• Workshops can be effective for hands-on learning and engagement.

• Choosing the right framework depends on the specific problem being solved.

• Observability is crucial for understanding AI systems' decision-making processes.

• Problem-solving should be prioritized over tool selection in AI development.

• Experimentation with tools is essential for effective AI engineering.

• A strong foundation in software engineering enhances problem-solving capabilities.

Connect with Alessandro Romano

• Alessandro Romano’s Website - https://www.aromano.dev/

  
  

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode of Let’s Talk AI, Miguel explains why hands-on learning beats passive theory, why systems thinking is the real skill every AI engineer needs, and why observability—understanding what’s happening inside your models and systems—is non-negotiable if you want to build responsibly.

For anyone looking to break into AI engineering, this episode offers a roadmap: stop chasing every trend, start focusing on systems, and above all…

Build, learn, and grow.

Top Insights:

• Neural Maze aims to help aspiring engineers navigate the overwhelming AI landscape.

• Building AI systems requires thinking in terms of systems, not just code.

• Observability is crucial for deploying AI systems in production.

• Learning by building projects is the most effective way to gain skills.

• Filo Agents project connects philosophy with gaming, showcasing innovative applications of AI.

• The value of code has shifted; engineers must adapt to new technologies.

• Miguel encourages staying out of the hype and focusing on building meaningful projects.

• He plans to expand Neural Maze into a community for self-contained courses.

Connect with Miguel Otero Pedrido

• Miguel Otero Pedirdo on LinkedIn

• Miguel Otero Pedrido on Substack

• Miguel Otero Pedrido on Medium.com

• Miguel Otero Pedrido on GitHub

• Miguel Otero Pedrido on X

• The Neural Maze on Substack

• The Neural Maze on YouTube

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn

• Let’s Talk AI

• Let’s Talk AI on YouTube

• Let’s Talk AI on Spotify

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode of Let’s Talk AI, Nathan Labenz joins us to share insights from years at the forefront of AI development. We cover everything from the disruptive potential of AI-powered content creation to the deep ethical debates around autonomy, alignment, and safety.

This is an urgent conversation because the decisions we make now—about adoption, guardrails, and governance—will define the next decade.

Top Insights:

• There is radical uncertainty about AI's future.

• Nathan Labenz's mission is to understand AI's current state.

• AI is transforming content creation and marketing.

• Hands-on experience with AI is crucial for understanding its capabilities.

• AI can provide services that were previously expensive and inaccessible.

• The ideal user experience is for AI to automate tasks seamlessly.

• AI's rapid adoption is unprecedented in history.

• Ethical considerations around AI consciousness are complex.

• AI can outperform humans in specific tasks, but has weaknesses.

• The future of AI holds both exciting opportunities and significant risks.

Connect with Nathan Labenz

• Nathan Labenz’ Website - https://www.nathanlabenz.com/

• Nathan Labenz on LinkedIn - https://www.linkedin.com/in/nathanlabenz

• Nathan Labenz on X - https://x.com/labenz

• The Cognitive Revolution - https://www.cognitiverevolution.ai/

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, the CEO of InEvent reveals why ideas are overrated, and why execution, adaptability, and timing matter far more. He shares unfiltered insights into securing funding, scaling globally, and leveraging AI-driven innovation to transform the events industry.

We also dive into remote leadership, the brutal realities of startup life, and the surprising role luck plays in success. If you’ve been fed the “follow your passion” narrative, this episode will challenge everything you thought you knew about entrepreneurship.

Top Insights:

• An idea's worth is tied to its revenue potential.

• AI can transform event technology in innovative ways.

• Starting a company often requires minimal initial investment.

• Persistence is key in securing funding and acceptance into accelerators.

• Founders must maintain a clear vision for their company.

• Effective decision-making involves prioritizing resources wisely.

• Market validation is crucial for product success.

• Iterative growth allows for focus and adaptation in business.

• Building an end-to-end platform enhances value propositions.

• Leadership in remote teams requires fairness and clear communication.

Connect with Pedro Goes

• Pedro Goes on LinkedIn - https://www.linkedin.com/in/pedrogoes/

• InEvent - https://inevent.com/

• Pedro Goes on X - https://x.com/goesinevent

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In our conversation with Jérémy Arancio, we dissect the frameworks he uses to thrive: relentless upskilling, leveraging platforms like LinkedIn for visibility, and distinguishing between hype and practical tools in AI. 

From the nuanced differences of GenAI vs. NLP to the tactical mindset shift freelancing requires, this episode breaks down what separates sustainable data science careers from those that fizzle out.

Top Insights:

• Freelancing is a means to learn and grow.

• You will never feel fully prepared to start freelancing.

• The journey of freelancing can be both rewarding and challenging.

• AI should be used to solve specific business problems.

• Content creation can help build connections and opportunities.

• Choosing the right vehicle for your career is crucial.

• Growth often comes from discomfort and challenges.

• Networking on platforms like LinkedIn is valuable for career advancement.

• Understanding the difference between Gen.AI and NLP is important.

• Continuous learning is essential in the fast-paced tech industry.

Connect with Jeremy Arancio

• Jeremy Arancio on LinkedIn - https://cz.linkedin.com/in/jeremy-arancio

• Read his articles - https://medium.com/@jeremyarancio

• Jeremy Arancio on GitHub - https://github.com/JeremyArancio

• Email him - jeremyarancio.freelance@gmail.com

  
  

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Thomas Bustos and Marijn Markus break down the complex interplay between innovation and responsibility in artificial intelligence and machine learning. They examine current limitations in explainability and bias, and unpack what it will take to build scalable, transparent, and human-centric systems.

With examples from healthcare and finance, and a strong focus on aligning AI with social good, this episode offers both technical depth and strategic perspective. Whether you’re designing ML pipelines or debating LLM regulation, this conversation delivers insight you won’t want to miss.

Top Insights:

• Ethical and Data Challenges: AI systems must navigate ethical considerations and data privacy concerns, ensuring transparency and accountability.

• Innovation and Impact: AI has the potential to revolutionize industries, from healthcare to finance, by driving advancements and creating new opportunities.

• Regulatory and Legacy Issues: Balancing innovation with necessary regulations and overcoming legacy systems are key challenges for AI adoption.

• Bias and Misinformation: Addressing bias in AI models and combating misinformation are critical for ensuring AI's positive impact on society.

• Job Market Evolution: While AI may automate some jobs, it also creates new opportunities, emphasizing the need for adaptability and skill diversification.

• Human Responsibility: The importance of human oversight in AI systems is crucial, as technology should serve humanity's best interests.

Connect with Marijn Markus

• Marijn Markus on LinkedIn - https://www.linkedin.com/in/marijnmarkus

• Capgemini - https://www.capgemini.com/ 

  
  

Connect with Thomas Bustos & Let's Talk AI

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Substack - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. 

We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. 

Clear, practical, and refreshingly honest. 

This episode is for anyone who wants to grow with intention.

Top Insights:

• Data engineering is at a pivotal moment, similar to the industrial revolution.

• The demand for data engineering skills is rapidly increasing.

• Understanding SQL is crucial as it constitutes the majority of data workloads.

• Feature prioritization should focus on high yield, low effort projects.

• Observability is essential for building reliable data systems.

• Scaling challenges often arise from unexpected user demand spikes.

• Product thinking is important in data engineering to meet user needs.

• GenAI has the potential to revolutionize data engineering practices.

• Exploring various aspects of data engineering is beneficial before specializing.

• Real-world observation can enhance understanding of data engineering concepts.

Connect with Ananth Packkildurai

• Ananth Packkildurai on LinkedIn - https://www.linkedin.com/in/ananthdurai

• Data Engineering Weekly by Ananth Packkildurai - https://www.dataengineeringweekly.com/ 

  
  

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

• Let’s Talk AI - https://thomasbustos.substack.com/ 

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

💡 In this episode...
... Reuven shares insights from his extensive career, discussing his current projects that focus on improving online courses and engaging learners through newsletters. He emphasizes the importance of project-based learning and understanding deeper concepts in technology, rather than just syntax. Reuven reflects on the challenges of traditional education and the need for innovative teaching methods. He also addresses the impact of AI on learning and the significance of hands-on experience in skill development. Listeners will find encouragement to embrace programming as a journey and learn how acquiring these skills can lead to fulfilling career opportunities.

Reuven Lerner: https://www.linkedin.com/in/reuven/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Follow Let's Talk AI:
🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/
📹 Youtube 👉 https://www.youtube.com/@lets-talk-ai
🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Peter Gostev is the Head of AI at Moonpig, specializing in generative AI and its transformative applications in e-commerce. With a strong background in structured problem-solving and data analytics, Peter emphasizes the importance of collaboration and rapid prototyping to drive innovation in AI projects.

💡 In this episode...

... we explore Peter's journey in the AI landscape, discussing the balance between delivering immediate value and fostering innovative experimentation. He shares insights on the distinctions between classical and generative AI, highlighting their respective use cases in structured and unstructured data. Peter elaborates on how generative AI can enhance data quality and operational efficiency, particularly in customer service through automation and insights extraction.

The conversation also addresses the challenges of integrating AI into software development, advocating for specialized teams to effectively manage new technologies. Peter encourages continuous learning and experimentation in AI, urging listeners to engage with emerging tools and models to drive innovation in their own practices.

Peter Gostev: https://www.linkedin.com/in/peter-gostev/

Follow Let's Talk AI:

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Daniel Lenton is the CEO of Unify, on a mission tounify and simplify the LLM landscape. He has a background in machine learning deployment, as well as in robotics and computer vision. Daniel is an intellectual and creative problem solver who values consistency, balance, and expectation management when building a startup.

💡 In this episode...

... we discuss Daniel's background in machine learning deployment and pursuit of interests in robotics and computer vision. He shares insights on effective communication, user discovery, and gaining insights from customer sales calls for building a successful start-up. He values consistency, balance, and expectation management and credits YC for giving him productivity tips to achieve this. He believes in prioritizing joy and ethics and acknowledges advancements and limitations in AI and AGI.

Daniel Lenton: https://www.linkedin.com/in/daniellenton/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Demetrios Brinkmann is the Founder and CEO of the MLOps Community, and also serves as a Conference Host. He is an expert in AI implementation for technical & non-technical professionals and has extensive knowledge of workflow optimization and AI automation. He has a passion for helping people find joy in AI implementation, and believes in augmenting expertise with AI instead of replacing it. Demetrios is also skilled in optimizing productivity through human-AI collaboration and effective decision-making.

💡 In this episode...

... we discuss AI incorporation and workflow optimization. Demetrios shares his expertise on improving efficiency and finding joy in AI implementation for professionals. Demetrios also emphasizes the importance of augmenting expertise with AI rather than replacing it, and optimizing productivity through human-AI collaboration. He provides insightful tips for improving case studies workflow, integrating AI in workflows, and optimizing web development processes. The episode also includes discussions on voice AI use cases, AI in customer service, and streamlining podcast production. Additionally, Demetrios shares his valuable insights on data engineering, financial operations, and MLOps in AI (and more!!).

Demetrios Brinkmann: https://www.linkedin.com/in/nozurbina/

MLOps Community: https://mlops.community/

Deepgram: https://deepgram.com/

Data Engineering for AI/ML Conference (12 september 2024): https://home.mlops.community/home/events/dataengforai?agenda_day=665ee46e12463aa4df4d48b2&agenda_track=665ee46f12463aa4df4d48c4&agenda_stage=665ee46e12463aa4df4d48b8&agenda_filter_view=stage&agenda_view=list

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Noz Urbina is a content value designer, strategist, and podcaster who specializes in helping enterprises leverage AI and omnichannel for building brands that have relationships with their customers and to foster communities digitally. He has over 14 years of experience in content strategy and customer experience consultancy, where he is a pioneer in adaptive content modelling for delivering personalized, contextually relevant content experiences in an omnichannel environment. Noz has coached teams, developed processes, and spearheaded solutions for some of the world's largest organizations such as Eli Lilly Pharmaceuticals, Barclays Bank, Microsoft, and many more. He is also the founder of Urbina Consulting and OmnichannelX.digital, where he facilitates community learning and enables cross-industry collaboration. Noz is well-known for his work on customer journey mapping, structured content, project planning and scoping, and high-tech product evangelism.

💡 In this episode...

... we discuss how AI is enhancing content creation and the entire content life cycle, specifically in journey mapping, persona creation, and lead scoring. Noz also shares insights on building adaptable AI frameworks, optimizing customer engagement through omnichannel design, and measuring success in patient-centered content projects.

Noz Urbina: https://www.linkedin.com/in/nozurbina/

Urbina Consulting: https://urbinaconsulting.com/

Omnichannel X: https://www.omnichannelx.digital/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Markus Stoll is an expert AI guest who is well-versed in creating advanced software solutions for the efficient exploration of unstructured data. Markus is the Co-Founder and CTO of Renumics, a company that specializes in developing open-source tools for visualization in Data-Centric AI. Not only has Markus extensive experience in solving complex problems using workflow and visualization, but he also has a strong engineering background too. From Engineer to CTO, Markus has taken his passion for AI and founded Renumics, which has evolved over the years, and today, it's successfully developing machine learning and visualization solutions.

💡 In this episode...

... we discuss Markus's experience in creating advanced software solutions for exploring unstructured data. Markus discusses how feedback, domain understanding, and foundational models can be used to build scalable data evaluation projects and recommendation systems. He also highlights the challenges and opportunities of building RAG systems, the importance of glossaries and future fine-tuning, and introduces Spotlight, a powerful open-source tool for comprehensive data analysis. Finally, Markus introduces an open-source audio data segmentation and labeling tool that shifts from 3D visualization to audio data labeling based on user feedback.

Markus Stoll: https://www.linkedin.com/in/markus-stoll-b39a42138/

Markus's Medium: https://medium.com/@markus.stoll

Github spotlight: https://github.com/Renumics/spotlight

Renumics: https://renumics.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Maxime Labonne is a Staff Machine Learning Scientist at Liquid AI. Maxime received his PhD from the Polytechnic Institute of Paris and has been working with machine learning since 2019. He has applied his expertise in various contexts, including R&D, industry, finance, and academia. He is also an AI/ML Google Developer Expert. Maxime is widely known for creating popular LLMs on Hugging Face, like AlphaMonarch-7B, Beyonder-4x7B, Phixtral, and NeuralBeagle14. He also released LLM tools, such as LLM AutoEval, LazyMergekit, LazyAxolotl, and AutoGGUF. Maxime is the author of the technical book "Hands-On Graph Neural Networks using Python," published with Packt. He has written technical articles on his blog and Towards Data Science and created the popular LLM course on GitHub, which has over 27,000 stars.

💡 In this episode...

... we discuss Maxime's expertise in large language models (LLMs) and graph neural networks, as well as his journey from cybersecurity to machine learning. We also talk about insights on LLMS misconceptions, evaluation optimization techniques, and creating augmented datasets for fine-tuning. Maxime also talked about graph neural networks and non-transformer architectures and provided advice for leading language model research and mentoring language model research beginners. Finally, we discuss Maxime's passion for open source and knowledge sharing.

Follow Let's Talk AI:

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Jakub Czakon is a senior data scientist turned CMO, currently working at neptune.ai. He has a strong background in AI and LLMs in marketing and has extensive knowledge on how to apply AI in marketing for practical implementation and trust navigation. Jakub draws inspiration from chess skill levels to teach us about data science and emphasizes the importance of learning by doing. He is also an expert in strategies for effective resource allocation and trust-building in marketing and Dev Rel, and he utilizes AI and data to inform decision-making and focuses on problem solving.

💡 In this episode...

... we discuss the application of AI in marketing for practical implementation and trust navigation. Jakub draws inspiration from chess skill levels to teach us about data science, emphasizes the importance of learning by doing, and shares strategies for effective resource allocation and trust-building in marketing and Dev Rel. Jakub also utilizes AI and data to inform decision-making and focuses on problem-solving.

Jakub Czakon: https://www.linkedin.com/in/jakub-czakon/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

📹 Youtube 👉 https://www.youtube.com/@lets-talk-ai

📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Andrew Jardine is the Head of GTM at Adaptive ML and an advocate for open-source ML. He is also the leader of the MLOps community in Toronto. Andrew's AI journey began as an early employee at Kira Systems, a Doc AI and NLP startup, where he developed the GTM playbook for the corporate market. He has since held commercial roles at Hugging Face, and worked in data science and MLOps industries. Andrew has a diverse background, having worked as a management consultant in London, qualified as a chartered accountant, and obtained a master's degree in engineering.

💡 In this episode...

... we discuss Andrew's fascination with gen AI use cases and LLMS advancements, and the challenges and benefits of implementing open-source AI. We talk about overcoming the challenges of open-source language models through an innovative LLM platform, evaluating LLMs for specific use cases, ensuring safety and toxicity, and more. Andrew also explains how open-source models can help large enterprises control costs and extract data, and discusses the choice between open or closed source models.

Andrew Jardine: https://www.linkedin.com/in/andrew-iain-jardine/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Alex Merced is a senior technical evangelist at Dremio and co-author of "Apache Iceberg: The Definitive Guide." With a diverse career path that spans from finance to developer advocacy, he is a seasoned expert in data engineering and open standards for unifying diverse data solutions. Alex is passionate about the open-source community and the excitement around data lake house technologies, which he specializes in.

💡 In this episode...

... we discuss data engineering and open-source technology as well as unified platforms, data lake houses, and how they can benefit AI and marketing analytics. Alex shares how Dremio's data lake house platform can optimize query performance, reduce costs, and increase scalability. He also gives advice on getting involved in the open-source community and creating value through curiosity and detours.

Alex Merced: https://www.linkedin.com/in/alexmerced/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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🌐 Website 👉 https://lets-talk-ai.com/

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Malcolm Hawker is the Head of Data Strategy at Profisee | Data Management. With over 25 years of experience in data and analytics, Malcolm is a recognized thought leader and advisor in the field of Master Data Management (MDM) and Data Governance. He has helped some of the world's largest businesses to improve their enterprise information management strategies and is passionate about sharing practical and actionable insights on how to leverage data as a strategic asset. Malcolm hosts the CDO Matters podcast where he interviews senior data leaders from various industries and domains and aims to raise the awareness and value of MDM and Data Governance globally, empowering companies with the best practices and solutions to overcome their data quality challenges and achieve their business goals.

💡 In this episode...

... we discuss the challenges and strategies for Chief Data Officers in building valuable data-driven products, quantifying the value of data and analytics, and improving data quality. We also explores the relationship between CIOs and CDOs, challenges in implementing Master Data Management (MDM), and the importance of data governance policies. Malcom emphasizes the role of governance in driving business success and measuring the effectiveness of data-driven decisions, as well as the limitations of marketing attribution models and effective use of MDM in AI implementation.

Malcolm Hawker: https://www.linkedin.com/in/malhawker/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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🌐 Website 👉 https://lets-talk-ai.com/

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Kyle is Head of Product at Onehouse.ai and ex-Azure Databricks. With over a decade of experience in managing teams of 2-60 people, Kyle is a proven product leader who specializes in data engineering and lake house architectures. He has scaled products for global enterprises to drive modernization of their data estates. Kyle also volunteered as an Entrepreneurship Coach and mentor for incarcerated and formerly incarcerated individuals. He has contributed to the open-source community by introducing the One Table project for interoperability and empowering positive change through collaboration.

💡 In this episode...

... we discuss Kyle's experience in data engineering and lake house architectures. He shares his insights on building data platforms and products, overcoming challenges and optimizing performance with One House, exploring diverse lake house table formats, empowering positive change through open source collaboration, scaling strategies, team alignment challenges at Databricks, and tips for beginners and advancing in the data engineering field.

Kyle Weller: https://www.linkedin.com/in/lakehouse/

Onehouse: https://www.onehouse.ai/

Apache XTable™ (incubating): https://xtable.apache.org/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

James Serra is a Technical Specialist at Microsoft with over 35 years of expertise in data warehousing and data management, making him a thought leader in the use and application of Big Data technologies. He is also well-versed in a number of data design concepts and methodologies, including normalized, relational, dimensional, and columnar approaches. Moreover, he has been a team leader and mentor for multiple large-scale, full life-cycle database implementations, from requirements and design through development, client acceptance and production. James is also a popular blogger, author, and speaker, having presented at PASS Summit, SQLBits, Enterprise Data World conference, Big Data Conference Europe, and dozens of SQL Saturdays. His specialties include business intelligence, data warehousing, big data, Hadoop, cloud, master data management, machine learning, solution architecture, ETL, data modeling, data migration, dashboarding, Kimball methodology, massively parallel processing, database architecture, data integration, and performance tuning among others.

💡 In this episode...

... we discuss his journey, the evolution of storing and reporting data, the use of AI technology for better business decisions and tips for staying up-to-date in the ever-changing world of technology and AI. We also delve into Microsoft's use of AI in providing insights on political advertisements.

James Serra: https://www.linkedin.com/in/jamesserra/

James's Book: https://www.amazon.com/Deciphering-Data-Architectures-Warehouse-Lakehouse/dp/1098150767

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

📹 Youtube 👉 https://www.youtube.com/@lets-talk-ai

📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Anna Skoulikari is a multi-faceted technical writer with expertise in git education and simplifying technical writing. She is also the author of a book titled 'Learning Git - A Hands-On and Visual Guide to the Basics of Git!' published by O'Reilly. Anna has a website and she also has a mailing list for those who want to receive updates on the things she does or creates. Anna is an excellent Git educator who prioritizes user experience and simplifying development goals. She has expertise in information architecture, API documentation, user guides, tutorials, and other markup languages. Anna emphasizes effective revision strategies, prioritizing user testing, and embracing imperfection and iteration in UX design in her work.

💡 In this episode...

... we discuss her expertise in technical writing and git education. She shares tips and strategies for simplifying technical writing and improving user experience in development goals. Anna emphasizes effective revision strategies and embracing imperfection and iteration in UX design. She encourages career pivoting and experimentation, offering sound advice for those in UX/UI/Technical Writing. Overall, Anna is an excellent Git educator with a multi-faceted and clear vision for writing.

Kunal Agarwal: https://www.linkedin.com/in/kunalkunal/

Learning Git: https://www.amazon.com/Learning-Git-Hands-Visual-Basics/dp/1098133919

Anna's Website: https://www.annaskoulikari.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/

🌐 Website 👉 https://lets-talk-ai.com/

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Kunal Agarwal is CEO of Unravel Data. He has a background in leading sales and implementation of Oracle products at Fortune 100 organizations. As CEO, he serves as both chief cheerleader and curator of Unravel's culture, while also keeping the team focused on customer needs. Unravel's Data Observability Platform provides comprehensive insights and tools to improve the efficiency and cost of data analytics projects for a variety of industries.

💡 In this episode...

...we discuss about simplifying cloud data operations, Unravel's Data Observability Platform insights and tools to improve data analytics efficiency and cost for various industries. I was super curious about how they enhance metadata, how the systems works and it where what's possible with this technology gen ai.

Kunal Agarwal: https://www.linkedin.com/in/kunalkunal/

Unravel: https://www.unraveldata.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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Ángel Sáenz de Cenzano is LinkedIn's the Country Manager for Spain and Portugal. Ángel is a visionary leader with extensive experience in driving growth and propelling change mainly in the tech industry.

💡 In this episode...

... we discuss Ángel's plans and leadership strategies, aligning with executives, personal branding, and maximizing LinkedIn's potential for personal and professional branding. We also touch on managing technical teams and career tips for succeeding in the tech industry.

Angel Sáenz de Cenzano: https://www.linkedin.com/in/angelsaenzdecenzano/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🎙️ Podcast 👉 http://smartlink.ausha.co/let-s-talk-ai/

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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Jaime Oliver Huidobro is a Lead Data Scientist at Clarity AI. He holds undergraduate and master's degrees in physics, mathematics, and economics, as well as an industrial PhD in mathematical engineering. Jaime's passion for environmental, social, and corporate governance (ESG) issues led him to conduct postdoctoral research at the MIT Aggregate Confusion Project, where he focused on identifying biases in corporate emissions disclosures. Currently, at Clarity AI, Jaime is leading the integration of advanced AI systems to enhance and scale the incorporation of ESG metrics into investment processes.

💡 In this episode...

... we discuss Jaime's passion for sustainability and corporate governance, and how he uses data science and AI to bring sustainability to financial markets. The conversation covers ESG ratings, sustainable investments, green assets, supply chain sustainability, challenges in solving regression problems and estimating emissions accuracy, evaluating model systems, and the impact of human augmentation and trade-offs in AI systems.

Jaime Oliver Huidobro: https://www.linkedin.com/in/jaime-oliver-huidobro/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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🌐 Website 👉 https://lets-talk-ai.com/

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Piethein is the Chief Data Officer for Microsoft in the Netherlands and has a vast amount of experience in data management. He has worked with leading corporations, such as ABN AMRO, and has established himself as a trusted advisor for C-level executives. Piethein is passionate about sharing his insights with the community and has authored "Data Management at Scale" (O'Reilly), which offers a comprehensive guide to data management trends. He is skilled in project management, enterprise architecture, data strategy, consultancy, and complex problem-solving, with expertise in data management, data engineering, domain-driven design, machine learning, cloud, microservices, and API management, and web development. Piethein is committed to helping his customers achieve their goals through innovative data solutions and strategic guidance.

💡 In this episode...

... we discuss Piethein's career journey from working with computers to implementing data mesh architecture. We talk about demystifying data management and his book on democratizing data, as well as the role of a Chief Data Officer and developing a balanced data strategy. Piethein also talks about effective communication strategies and building relationships, addressing fear, and building trust for AI adoption, including Microsoft's approach to responsible AI.

Piethein Strengholt: https://www.linkedin.com/in/pietheinstrengholt/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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Raphaël is a data scientist and ML engineer (tech lead + trainer) who is highly skilled in leadership, team building, and fostering a positive working culture. Raphaël's passion for developing and nurturing young talent is evident through his mentorship and guidance of interns and junior colleagues, and his network of clients includes prominent names such as Rabobank, Heineken, and the European Commission.

💡 In this episode...

... we discuss insights on MLOps, leadership, team building, and fostering a positive working culture. We also touch on emphasizes automation, stakeholder engagement, technical responsibility, and effective communication in MLOps projects, and stresses the need to address biases and be mindful of hype in AI. Raphaël also provides valuable tips for mastering MLOps and combating imposter syndrome, and highlights the importance of creating joyful mini-cultures to build successful MLOps teams and nurture young talent.

Raphaël Hoogvliets: https://www.linkedin.com/in/hoogvliets/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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Ole Olesen-Bagneux is globally recognized thought leader on data catalogs and enterprise data management. He is an O'Reilly author, with his book "The Enterprise Data Catalog" (2023). Ole holds a Ph.D. from the University of Copenhagen, in Library- and Information Science, and brings a wealth of hands-on data management and mesh experience to the conversation.

💡 In this episode...

... we discuss Ole's journey, the concept of Federated Knowledge Graph and Data Mesh as a decentralized approach to data management. We also talk about the benefits of a Knowledge Graph Metadata Layer and the importance of historical analysis and metadata tools in understanding Data Culture. Ole's shares the importance to NOT rely solely on technology for Data Mesh and talk about the open learning environment within the data community.

Ole Olesen-Bagneux: https://www.linkedin.com/in/ole-olesen-bagneux/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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Oleksandr Honchar is the Director of AI Engineering and Partner at Neurons Lab with over 10 years of experience in building science-heavy products. His mission is I help deep tech innovators to speed up AI R&D and build disruptive products. Previously he worked as an independent research engineer and tech leader (mostly with medical devices and in investment management) and taught AI/ML at universities and public events.

💡 In this episode...

... we discuss the intersection of technology and culture and its impact on his AI business approach. Oleksandr shares his journey to founding Neurons Lab and empowering organizations with ethical AI. We also get insights on developing a diverse skill set and building successful teams for effective leadership and management. Oleksandr talks about the future of AI and its impact on society, the rise of multi-modal models in machine learning, and the challenges and warnings for machine learning in finance. And much more!

The 5-Level AI Spectrum: https://www.nfx.com/post/ai-perspective-2023

Oleksandr Honchar: https://www.linkedin.com/in/alexandr-honchar/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Apoorva Joshi is a Senior AI/ML Developer Advocate at MongoDB. With a Bachelor's and a Master's degree in Engineering, Apoorva has over 5 years of experience in applying ML to real-world problems in different industries including cybersecurity.

💡 In this episode...

... we discuss machine learning problems and approaches in cybersecurity, the field of data/ai, putting models in production, career tips and effective networking techniques for AI professionals. Apoorva also shares insights on job hunting strategies and the role of developer relations in AI.

Apoorva Joshi: https://www.linkedin.com/in/apoorvajoshi95/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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Houssem Chihoub is a Solutions Architect (Data and AI) at Databricks and have previously a Specialist Solutions Architect at AWS (Analytics). Houssem also have a Ph.D and had an early access to AWS around 2010 (fun fact hihi).

💡 In this episode...

... we discusses the benefits of cloud computing and working for tech pioneers in this field, as well as the differences between cloud computing and on-premise deployment, and new technologies in the market. Houssem shares insights on his career path and evolution with Hadoop, transitioning from academia to the business world, and the qualities of a great solutions architect. He emphasizes understanding business metrics and outcomes, identifying business pain points, and designing effective solution architectures, all while maintaining agility and an iterative mindset. He also discusses data engineering trends, Lake Houses, and Data Mesh, as well as exciting AI developments and the importance of balancing responsible use and innovation, and balancing regulation. Overall, Houssem is a valuable expert in the data and AI space, with a wealth of knowledge and experience to share.

Houssem Chihoub: https://www.linkedin.com/in/houssem-chihoub/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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Médéric Hurier is a Lead MLOps Engineer with a Ph.D. in malware detection leveraging ML.

His is also Freelance AI/ML/MLOps/LLMOps Engineer and Data Scientist. Médéric worked and collaborated with industry giants such as Google, BNP Paribas, ArcelorMittal, the European Commission, and Decathlon. He is also a dedicated MLOps Community Organizer and OpenClassrooms Mentor.

💡 In this episode...

... we discuss Médéric's experiences working with companies to develop AI/ML solutions at scale. Médéric discusses handling big data processing, building effective MLOps platforms, bridging academia and industry, and developing AI models for healthcare while addressing ethical considerations. He also shares best practices for building and choosing MLops platforms, building pricing models and fraud detection, and his views on continuous improvement and community building in MLOps. (a lot to learn from this episode ahah)

Médéric Hurier: https://www.linkedin.com/in/fmind-dev/

Médéric's Website: https://www.fmind.dev/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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Christophe Blefari is a software/data engineer with over 8 years of experience and expertise in Python, Django, and JS/React, as well as 5 years of data hacking experience in Pandas, Hadoop, SQL, cloud/on-premise architecture, Spark, Airflow, dbt, BigQuery, and Snowflake. He is an advocate for DataOps and team leadership and also specializes in Python and Airflow.

💡 In this episode...

... we discuss Christophe's journey. As a freelancer, he emphasizes the importance of teaching and shares tips on finding missions. As a freelancer, Blefari shares tips on how to find missions as a freelancer and emphasizes the importance of teaching in AI. He also discusses the ethical considerations and legal challenges in data engineering and provides insights into data regulation and Python preferences. Finally, he focuses on the importance of building strong teams and empowering them through management to successfully deliver projects. Overall, Blefari's experience and expertise provide valuable insight into the current landscape of data engineering.

Christophe Blefari: https://www.linkedin.com/in/christopheblefari/

Christophe's Youtube Channel: https://www.youtube.com/channel/UCwWEMzZbCZeYfRBV5JiCd9Q

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/

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🌐 Website 👉 https://lets-talk-ai.com/

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Leon is the Founder and CEO of Onyx Data, which provides end-to-end AI and data solutions, strategy and training for businesses looking to maximise their data insights. With a focus on helping clients unlock the true potential of their data, Onyx Data develops and executes high-performing data and analytics strategies through their Horizon packages, undertakes full-service data mesh deployments with their Vision solution, and runs Microsoft Fabric, Azure, Excel, and Power BI training and workshops.

💡 In this episode...

...we discuss AI and data analytics for business success, carving a path in data analytics and AI, empowering businesses with data insights, and Onyx Data's personalized and customized AI solutions for successful client engagement. Leon also shares Onyx Data's methodology for breaking down barriers to data literacy, their adoption of decentralized data architecture (Data Mesh), and his involvement in Microsoft MVP and Forbes Tech Council. Additionally, we discuss about Onyx Data's Data DNA Dataset Challenge, personal branding in the data industry, and the importance of online contribution.

⏳ Chapters

00:00 - Introduction

00:00:54 - AI and Data Analytics for Business Success

00:02:03 - Leon Gordon's Journey

00:08:47 - The Entrepreneurial Journey of Leon Gordon, Leader of Onyx Data

00:12:11 - Data-Driven Solutions

00:12:52 - Onyx Data's Bespoke Methodology for Successful Client Engagement

00:19:12 - Breaking Down Barriers to Data Literacy

00:26:55 - Decentralized Data Architecture and Real-Time Analytics

00:32:40 - Successful Adoption of Decentralized Data Architecture

00:34:08 - Decentralized Data Architecture with Data Mesh and Involvement in Microsoft MVP and Forbes Tech Council

00:38:39 - Onyx Data's Data DNA Dataset Challenge

00:44:34 - Data DNA Dataset Challenge and Personal Branding in the Data Industry

00:48:38 - Personal Branding, Online Contribution, Data DNA Dataset Challenge and message from Leon

Leon Gordon: https://www.linkedin.com/in/leon-gordon/

Data DNA Challenge: https://onyxdata.co.uk/data-dna-dataset-challenge/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Anastasiia Prokaieva is an AI and GeoSpatial Specialist at Databricks, enthusiastic and autonomous ex-Research Scientist who turned her interests towards Data Science and Big Data in recent years. With her background in Experimental Nuclear Physics, Plasma Physics, Theoretical Physics, and Applied Mathematics, as well as Renewable Energy Science and Technologies.

💡 In this episode...

...we discuss various topics ranging from her career path and experiences as an ML Engineer at Databricks, to soft skills development and handling big data in the academic AND private sector. She also delves into the importance of algorithm planning, collocating data, and utilizing custom libraries to achieve better ML results. Anastasiia also touches on the future of AI and ML jobs, the fundamentals and practical uses of AI tools, and lessons learned from her amazing journey.

Anastasiia Prokaieva: https://www.linkedin.com/in/anastasiia-prokaieva/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Neelesh is a Principal Software Engineer at Salesforce, with experience in software and data infrastructure engineering. He has a passion for building distributed systems and product engineering, with a focus on customer experience.

💡 In this episode...

...we discuss Neelesh's journey in data infrastructure and technical leadership, his perspective on observability and learning in data engineering, trends in big data tool evolution and management and the current status of software engineering. We also discuss the importance of communication, documentation, and effective code review, as well as building close customer relationships and learning from early feedback in software development. Neelesh also shares his advice for data engineers on learning, growth, and adaptability, and how to maintain a work-life balance in the field of data engineering and AI.

Neelesh Salian: https://www.linkedin.com/in/neeleshsalian/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas Follow

Let's Talk AI:

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💡 In this episode...
... we discuss Simon's journey of becoming an open source data engineer, the importance of design patterns and data architecture in modern data engineering, and the key components and tools for building the foundation of modern data engineering. We also discuss about the future evolution of data querying and storage optimization and shares tips for aspiring data engineers. Simon is also an author and blogger, and he discusses his writing process and how it helps him to organize and share his knowledge with others. He shares his tips for streamlining tasks and boosting productivity with tools like Obsidian and Vim Motions and discusses stoicism and minimalism.

Simon Späti: https://www.linkedin.com/in/sspaeti/
Simon's NEW book DEDP (free): https://www.dedp.online/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:
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Felix Laumann: https://www.linkedin.com/in/felix-laumann/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Megan Lieu: https://www.linkedin.com/in/andlukyane/

Thomas Bustos: https://www.linkedin.com/in/meganlieu/

Artistic Direction & Video: Maxence Kerhoas

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Maria Vechtomova is a MLOps Tech Lead passionate about streamlining processes and enhancing collaboration using MLOps. She has extensive 10+ years of experience, leading MLOps transformation and helping brands boost their MLOps maturity while improving security and transparency when it comes to ML model deployments. Maria share her thoughts and experiences through public speaking and Marvelous MLOps.

💡 In this episode...

...we discuss the benefits of MLOps in streamlining processes and enhancing collaboration. Maria shares insights about the challenges of implementing MLOps practices and how to prioritize improvement efforts with actionable insights. We talk about the importance of continuous monitoring and observability in MLOps implementation, as well as the need for frameworks and standardization for efficient model deployment. We approach how MLOps is an organizational challenge and how Maria shares her knowledge through Marvelous MLOps. Additionally, Maria talks about the importance of leadership in AI development and shares career advice and networking tips for MLOps professionals.

Maria Vechtomova: https://www.linkedin.com/in/maria-vechtomova/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Raphaël Mansuy is a seasoned data and AI strategist with over 20 years of global technology leadership experience. He is on a mission to democratize data management and AI, empowering organizations to build robust, scalable data platforms that drive transformative business outcomes. With his consultative approach, he deeply understands the unique requirements and goals of his clients and crafts tailored roadmaps to build, optimize, and scale their data infrastructure and capabilities. Raphaël's expertise in data engineering, machine learning, and product development has contributed to over €2B in revenue through data-driven software products and successful growth and sale of a company for 140x its initial investment. He is passionate about empowering people and organizations to maximize the value of their data, leading AI innovation, and creating data-centric cultures. Raphaël currently spearheads a startup studio in Hong Kong and offers consulting services to clients globally.

💡 In this episode...

...Raphaël shares his journey as an entrepreneur, his expertise in data engineering, machine learning, and product development. He discusses the evolution and specialization of computer science, the importance of creativity and caretaking roles in the age of AI, and responsible use and ethical considerations in AI development. Raphaël also emphasizes the importance of improving knowledge in AI through daily research and public learning, and how AI can be a tool for solving challenges and creating opportunities. Furthermore, we discuss about building top-down data systems for a specific use case, selecting metrics for AI success and evaluating performance, leveraging AI to address pain points in industries, and enhancing human skills and collaborative intelligence with AI agents.

Raphaël Mansuy: https://www.linkedin.com/in/raphaelmansuy/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

Follow Let's Talk AI:
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Andrey Lukyanenko is a data scientist with a strong track record in data science. He has a result-driven mindset and has previously worked as an ERP-system consultant, which has given him a deep understanding of various aspects of business operations. Andrey is a Kaggle Competition Master and has achieved the 1st rank in kernel ranking. He is passionate about applying deep learning and AI to develop applications that not only bring value to businesses but also improve the lives of users and clients. Andrey has implemented AI in various projects, including a successful medical chatbot. He believes in improving machine learning practice through sharing and participating in Kaggle competitions.

💡 In this episode...

... we discuss Andrey's journey into data science and success on Kaggle and the benefits of sharing and participating in ml competitions to improve. Andrey also explains his experience in developing a successful medical chatbot with named entity recognition and implementing AI in fraud detection. He emphasizes the significance of defining business metrics for data observability and the importance of soft skills and diverse experience in data science.

Andrey Lukyanenko: https://www.linkedin.com/in/andlukyane/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Jamshaid Sohail is a data science master's graduate who is experienced in solving data science problems in the industry for four years. He is an alumnus of several renowned universities, including RWTH Aachen University (Germany), University of Cambridge (England), Lahore University of Management and Sciences (Pakistan), National University of Computer and Emerging Sciences (Pakistan), and Information Technology University (Pakistan). Jamshaid has authored multiple online video courses and books, including "Data Wrangling with Python 3.x," "An Introductory Guide to Data Science and Machine Learning," "Data Science Course for Kids with Python," and "PyTorch for Deep Learning." He has reviewed nine books with Packt before they were published. Jamshaid has also earned a silver medal in the SIIM-ISIC Melanoma Classification competition on Kaggle.

💡 In this episode...

...we discuss Jamshaid's journey to becoming a data scientist, building and creating successful courses as an instructor. He shares insights about multiple projects including autonomous shopping, and discusses the challenges and baseline solutions, as well as the terminologies and libraries for autonomous shopping. We also discuss about real-time data management at the Dusseldorf Expo, providing Kaggle competition insights, and his tips, resources, and key players in the data science and Kaggle competition industry.

Jamshaid Sohail: https://www.linkedin.com/in/jamshaid-sohail-1a3905115/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Agostino Calamia is a Senior Data Scientist at Shopify with experience in the entire machine learning lifecycle. Agostino has been pivotal in scaling MVPs to 7-digit ARR products. In 2019, he transitioned from a Project Manager position to Data Science as he fell in love with the analytical part of businesses. Since then, he has worked with data teams for small, medium, and large-sized companies and experienced Multi-Billion Dollar Corporations, including N26 and Shopify. Agostino's experience in evaluating business requirements, running experiments, building data, and machine learning models, evaluating performance, and business impact, deploying the model, and setting up long-term monitoring have made him an expert in everything regarding the machine learning lifecycle. He has used various technologies, including Python & SQL, Git, BigQuery & Amazon Redshift, Apache Kafka, dbt, TensorFlow & PyTorch, to mention a few. Agostino is the author of  "The Playbook for Machine Learning Lifecycles," which aims to develop and deploy machine learning efficiently and robustly for companies that build user-facing models.

💡 In this episode...

...we discuss Agostino's experience and expertise on the entire machine learning lifecycle. He emphasizes the importance of ethical use of prompt engineering and understanding data science holistically. He also advises on advancing learning, streamlining ML model deployment, and transitioning from a data scientist to a machine learning engineer. Agostino stresses the importance of defining evaluation metrics, aligning business goals, and prioritizing AI projects for better adoption. He also emphasizes effective communication and collaboration strategies for AI teams and building reliable models. Also, we talked about the playbook for machine learning lifecycles that he wrote as well as his journey on sharing his learning with the community.

Agostino Calamia: https://www.linkedin.com/in/agostino-calamia/

Agostino's website: https://www.agostinocalamia.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Susan Shu Chang: https://www.linkedin.com/in/susan-shu-chang/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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💡 In this episode...
... we discuss Mehdi's philosophy on learning and continuous development in the tech industry, ways to improve technical skills for job interviews, content creation around tech/data and the value of time and family. The episode also explores the evolution of content creation and personal branding in the AI generation, the role of DevRel in the tech industry and its evolution, and soft skills and global perspectives for career advancement. Additionally, Mehdi shares insights into managing risk, time, and personal life and tips for finding jobs in data and AI roles.

Mehdi's LinkedIn: https://www.linkedin.com/in/tobias-zwingmann/
Medhi's Youtube Channel: https://www.youtube.com/@mehdio
Website Data Creators Club: https://datacreators.club/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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💡 In this episode...
... we discus Tobias's vision for AI and the importance of recognizing the human element in data science. Tobias shares his transition into AI and approaches to AI implementation for businesses, including prototyping and failing fast, iterating quickly, and balancing innovation and efficiency. Tobias also talks about his work with Rapid AI, a boutique agency that implements profitable AI use cases in 8 weeks or less for small-medium businesses, and the potential of GPT models for personal development and creative tasks. Finally, Tobias emphasizes the importance of building and investing in projects for maximizing project success through sharing, sustainability, and investment. AND MUCH MORE!!! :)

Tobias Zwingermann: https://www.linkedin.com/in/tobias-zwingmann/
Tobias's book: https://www.amazon.es/gp/product/1098111478/ref=dbs_a_def_rwt_hsch_vapi_taft_p1_i0
Rapyd AI: https://www.rapyd.ai/

Let's Talk AI Team
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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📷 Instagram 👉 https://www.instagram.com/lets_talk_ai/
🎞️ TikTok 👉 https://www.tiktok.com/@letstalkai/
🌐 Website 👉 https://lets-talk-ai.com/

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💡 In this episode...
...  we discuss Tales's career goals and experience in AI and machine learning, offering advice for data professionals on improving as machine learning engineers. Tales emphasizes the importance of ensuring ML solutions deliver value and align with business metrics, and stresses feedback, monitoring, and effective communication. Tales also shares a use case of using BERT for psychology tests and discusses potential threats of AI. He highlights the importance of open source and community-driven software in the future.

Tales Marra: https://www.linkedin.com/in/talesmarra/
MLOps101 ebook: https://talesmarra.gumroad.com/l/mlops101
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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💡 In this episode...
... we discuss about Andreas's journey into Data Engineering offering tips for those interested in pursuing a career in the field. He emphasizes the importance of mastering fundamental concepts, developing a distinct online presence, and focusing on growth and hands-on learning (get projects in you github!). Andreas also delves into the differences between Data Engineering and Data Architecture, and the cost management challenges faced by start-ups and large organizations. Finally, he addresses the challenges of securing Data Engineering systems and provides advice on how to become a Data Engineer and ensure Data Engineering security.

Andreas Kretz: https://www.linkedin.com/in/andreas-kretz/
Andrea's Academy: https://learndataengineering.com/p/academy
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Boyan Angelov is an experienced business and technology leader with a unique and amazing journey into data science, data strategy and is the author of the two books: "Python and R for the modern data scientist" (O'Reilly) and "Elements of Data Strategy". He have a robust history in architecting and implementing strategic solutions across a variety of organizational scales.

💡 In this episode...

... we discuss Boyan's journey and insights about data science and data strategy. He highlights the importance of coding to reconcile with math and discusses the significance of R and Python in data science and data strategy. Boyan emphasizes the significance of strategy in data science and engineering and how to deliver value and measure success in data science. He also stresses the importance of choosing valuable skill sets and implementing dynamic data strategies. Boyan shares his insights on understanding systems and avoiding the pitfalls of pilot projects in data strategy. He compares and contrasts the roles of a CTO and Chief Data Officer in AI and talks about building a culture of trust in data strategy implementation. Boyan emphasizes the importance of making career decisions based on one's own values and desires and the need to find a mentor aligned with one's vision.

Boyan Angelov: https://www.linkedin.com/in/angelovboyan/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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💡 In this episode...
... we discuss the importance of bridging the gap between academia and the real world in AI, building strong data foundations for effective decision-making, and connecting math and AI through an unbiased lens. We also cover the role of programming in infrastructure and data organization, the future of prompt engineering and language models, and the key qualities for successful data scientists. Lastly, Hala emphasizes clear communication, an ethical approach, and hands-on experience with real-world data for successful AI and data science projects.

Hala Nelson: https://www.linkedin.com/in/hala-nelson/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Panos Alexopoulos: https://www.linkedin.com/in/panosalexopoulos/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Dipankar is a passionate Data Engineering and Data Science Advocate and a community-focused individual currently working at Dremio. His primary focus is to assist engineering teams in building and scaling robust data platforms using open-source solutions like Apache Iceberg, Apache Arrow & Project Nessie. Dipankar strongly advocates data practitioners about Dremio's Lakehouse platform. Besides, he also led the R&D Advocacy team at Qlik, where he worked on key developer strategies and educated the worldwide developer community in the area of Machine Learning/Visualization. Dipankar has a keen interest in the field of Visual Analytics research, and his latest work was on Explainability of Ensemble Models using multidimensional projection techniques. Being exposed to different roles in the past, some of his key projects include implementing visualization solutions, developing production-ready supervised & unsupervised Machine Learning algorithms, evaluation and deployment of Visual Analytics solutions, and explaining ML algorithms. Dipankar believes in prioritizing core concepts and focusing on business problems over technology, and he has vast experience in incorporating open Lakehouse architecture into data warehousing.

💡 In this episode...

In this episode we discuss his passion for data and technology, his journey from Big Data Engineering to Developer Advocacy, and the benefits and challenges of Cloud Data Lakes for Compute Engines. He also highlights the Evolution of Big Data and the challenges of incorporating machine learning and streaming into data warehousing, advocating for an open Lakehouse architecture. Dipankar also provides practical applications for Apache Iceberg and shares his insights from real-world experience on how to enhance data platform functionality with seamless integration of Apache Iceberg. He also discusses the challenges of adopting new technology in big companies and identifies key factors for a successful cloud migration strategy implementation. He prioritizes core concepts and business problems over technology and advocates for focusing on core concepts over tools.

Dipankar Mazumdar: https://www.linkedin.com/in/dipankar-mazumdar/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Andy Petrella: https://www.linkedin.com/in/andypetrella/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Paul Iusztin is a machine learning engineer with over 5 years of experience in the industry with a background in software engineering, and mathematics. He is passionate about building and engineering AI systems using MLOps good practices and active iteration problem-solving processes. Paul have strong experiences working in different machine learning job positions and offers tips and advice for individuals looking to enter the AI field. His mission? To build data-intensive AI/ML systems that innovate the world, as well as his passion for helping other machine learning engineers level up through practical posts, articles, and tutorials. Additionally, Paul published The Full Stack 7-Steps MLOps Framework and MLOps-related topics, including version control with Git and using Airflow for automating machine learning.

💡 In this episode...

Paul discusses building and engineering AI systems using MLOps practices and active iteration problem-solving processes. He also shares his journey through different machine learning job positions and provides tips for entering the AI field. He emphasizes the importance of effective communication with customers and deploying multiple versions of AI/ML projects. Paul talks about the MLOps framework he published, and enhances learning efficiency with ChatGPT. An amazing episode for all MLOps and ML Engineers!

Paul Iusztin: https://www.linkedin.com/in/pauliusztin/
🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: https://github.com/iusztinpaul/energy-forecasting

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Nils Reimers: https://www.linkedin.com/in/reimersnils/
Cohere: https://cohere.com/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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Adi Polak: https://www.linkedin.com/in/polak-adi/

Scaling Machine Learning with Spark: https://www.oreilly.com/library/view/scaling-machine-learning/9781098106812/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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Hosted on Ausha. See ausha.co/privacy-policy for more information.

💡 In this episode...
In this episode we received Peter Cotton, a highly accomplished and experienced Quant who currently leads data science for Intech Investments. He discusses his work on the theory and practice of portfolio construction and his invention of Schur Portfolio allocation. Peter has also created and maintained various microprediction projects, founded Benchmark Solutions, and worked at J.P. Morgan, where he pioneered control theory application to OTC trading and privacy-preserving Machine Learning. He shares his insights on the adoption and implementation of data science technology in finance and his entrepreneurial journey and experience in the hedge fund industry. Peter also talks about the importance of markets in prediction and reflects on AI regulation, capabilities, and future implications. 

Peter Cotton: https://www.linkedin.com/in/petercotton/
Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/ 

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💡 In this episode...

In this episode we receive Francesco Zuppichini, Machine Learning Engineer with a passion for AI and deep learning. Francesco shares his experiences and insights on various topics, including hands-on learning, effective learning approaches, reading research papers, and AI project organization. He emphasizes the importance of open-mindedness in programming language choice and deep explanations in data science. Francesco also shares his thoughts on the future of AI and computer vision, stressing the importance of open source code, transparency, and real solutions.

Francesco Zuppichini: https://www.linkedin.com/in/francesco-saverio-zuppichini-94659a150/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

Artistic Direction & Video: Maxence Kerhoas

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🌐 Website 👉 https://lets-talk-ai.com/

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Dylan Anderson is a data strategy consultant and industry leader with a passion for bridging the gap between data and strategy. Dylan shares his journey to becoming a consultant, specializing in election analytics and data strategy consulting. He emphasizes the importance of building a strong foundation for data analytics and AI success, prioritizing meaningful metrics for client engagement and effective communication of data insights. Dylan also discusses the challenges in implementing new AI models and the importance of human oversight in AI development and regulation. With his R coding and strategy consulting experience, he helps bridge the gap between data scientists and business executives. 

💡Episode Summary: 

In this episode, Thomas interviews Dylan Anderson, a data strategy consultant and industry leader, about his journey to becoming a consultant, his specialization in election analytics and data strategy consulting, and the challenges of implementing new AI models. Dylan emphasizes the importance of building a strong foundation for data analytics and AI success, prioritizing meaningful metrics for client engagement, and effective communication of data insights. He also discusses the importance of human oversight in AI development and regulation and how he helps bridge the gap between data scientists and business executives with his R coding and strategy consulting experience. Dylan's unique insights make this an informative and engaging conversation for anyone interested in data strategy and AI.

Dylan Anderson: https://www.linkedin.com/in/dylansjanderson/ 

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/ 

Artistic Direction & Video: Maxence Kerhoas 

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🌐 Website 👉 https://lets-talk-ai.com/

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💡 Episode Summary:
Andrea's insights in this episode are nothing short of transformative. She shares her experiences in utilizing social media data to aid rescue efforts during the Mexico earthquake, highlighting the immense power of data intelligence, validation, and governance in government AI projects. Moreover, Andrea emphasizes the vital importance of ethics guidelines and social awareness while implementing AI solutions, providing career advice that underscores the social impact and significance of AI regulation.

Andrea Barenque: https://www.linkedin.com/in/andrea-barenque/
Datamorfosis: https://www.datamorfosis.mx/

Thomas Bustos: https://www.linkedin.com/in/thomasbustos/

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