On this episode of Learning from Machine Learning, I had the privilege of speaking with Maxime Labonne, Head of Post-Training at Liquid AI. We traced his journey from cybersecurity to the cutting edge of model architecture. Maxime shared how the future of AI isn't just about making models bigger—it's about making them smarter and more efficient.

Maxime's work demonstrates that challenging established paradigms requires taking steps backward to leap forward. His framework for data quality—accuracy, diversity, and complexity—offers a blueprint for anyone working with machine learning systems.

Most importantly, Maxime's perspective on learning itself—treating knowledge acquisition like training data exposure—reminds us that growth comes from diverse, high-quality experiences across different contexts. Whether you're training a model or developing yourself, the principles remain remarkably similar.

Thank you for listening. Be sure to subscribe and share with a friend or colleague. Until next time... keep on learning.

00:46 Introduction and Maxime's Background

01:47 Journey from Cybersecurity to Machine Learning

03:30 The Fascination with AI and Cyber Attacks

06:15 Transitioning to Post-Training at Liquid AI

08:17 Liquid AI's Vision and Mission

10:08 Challenges of Deploying AI on Edge Devices

13:06 Techniques for Efficient Edge Model Training

15:44 The State of AI Hype and Reality

19:19 Evaluating AI Models and Benchmarks

24:09 Future of AI Architectures Beyond Transformers

31:05 Innovations in Model Architecture

36:28 The Importance of Iteration in AI Development

39:24 Understanding State Space Models

42:53 Advice for Aspiring Machine Learning Professionals

48:53 The Quest for Quality Data

52:56 Integrating User Feedback into AI Systems

58:13 Lessons from Machine Learning for Life

On this episode of Learning from Machine Learning, I had the privilege of speaking with Aman Khan, Head of Product at Arize AI. Aman shared how evaluating AI systems isn't just a step in the process—it's a machine learning challenge in of itself. Drawing powerful analogies between mechanical engineering and AI, he explained, "Instead of tolerances in manufacturing, you're designing for non-determinism," reminding us that complexity often breeds opportunity.

Aman's journey from self-driving cars to ML evaluation tools highlights the critical importance of robust systems that can handle failure. He encourages teams to clearly define outcomes, break down complex systems, and build evaluations into every step of the development pipeline.

Most importantly, Aman's insights remind us that machine learning—much like life—is less deterministic and more probabilistic, encouraging us to question how we deal with the uncertainty in our own lives.

Thank you for listening. Be sure to subscribe and share with a friend or colleague . Until next time... keep on learning.

Available on Youtube: https://youtu.be/v0eTTn7ZPEc

Available on Substack: https://mindfulmachines.substack.com/p/aman-khan-arize-evaluating-ai-designing?r=eykwy

In this episode of Learning from Machine Learning, we explore the intersection of pure mathematics and modern data science with Leland McInnes, the mind behind an ecosystem of tools for unsupervised learning including UMAP, HDBSCAN, PyNN Descent and DataMapPlot. As a researcher at the Tutte Institute for Mathematics and Computing, Leland has fundamentally shaped how we approach and understand complex data.

Leland views data through a unique geometric lens, drawing from his background in algebraic topology to uncover hidden patterns and relationships within complex datasets. This perspective led to the creation of UMAP, a breakthrough in dimensionality reduction that preserves both local and global data structure to allow for incredible visualizations and clustering. Similarly, his clustering algorithm HDBSCAN tackles the messy reality of real-world data, handling varying densities and noise with remarkable effectiveness.

But perhaps what's most striking about Leland isn't just his technical achievements – it's his philosophy toward algorithm development. He champions the concept of "decomposing black box algorithms," advocating for transparency and understanding over blind implementation. By breaking down complex algorithms into their fundamental components, Leland argues, we gain the power to adapt and innovate rather than simply consume.

For those entering the field, Leland offers poignant advice: resist the urge to chase the hype. Instead, find your unique angle, even if it seems unconventional. His own journey – applying concepts from algebraic topology and fuzzy simplicial sets to data science – demonstrates how breakthrough innovations often emerge from unexpected connections.

Throughout our conversation, Leland's passion for knowledge and commitment to understanding shine through. His approach reminds us that the most powerful advances in data science often come not from following the crowd, but from diving deep into fundamentals and drawing connections across disciplines.

There's immense value in understanding the tools you use, questioning established approaches, and bringing your unique perspective to the field. As Leland shows us, sometimes the most significant breakthroughs come from seeing familiar problems through a new lens.

Resources for Leland McInnes

Leland’s Github

• UMAP
• HDBSCAN
• PyNN Descent
• DataMapPlot
• EVoC

References

• Maarten Grootendorst
  • Learning from Machine Learning Episode 1
• Vincent Warmerdam - Calmcode
  • Learning from Machine Learning Episode 2
• Matt Rocklin
• Emily Riehl - Category Theory in Context
• Lorena Barba
• David Spivak - Fuzzy Simplicial Sets
• Improving Mapper’s Robustness by Varying Resolution According to Lens-Space Density

Learning from Machine Learning

• Youtube
• https://mindfulmachines.substack.com/

In this episode, we are joined by Chris Van Pelt, co-founder of Weights & Biases and Figure Eight/CrowdFlower. Chris has played a pivotal role in the development of MLOps platforms and has dedicated the last two decades to refining ML workflows and making machine learning more accessible.

Throughout the conversation, Chris provides valuable insights into the current state of the industry. He emphasizes the significance of Weights & Biases as a powerful developer tool, empowering ML engineers to navigate through the complexities of experimentation, data visualization, and model improvement. His candid reflections on the challenges in evaluating ML models and addressing the gap between AI hype and reality offer a profound understanding of the field's intricacies.

Drawing from his entrepreneurial experience co-founding two machine learning companies, Chris leaves us with lessons in resilience, innovation, and a deep appreciation for the human dimension within the tech landscape. As a Weights & Biases user for five years, witnessing both the tool and the company's growth, it was a genuine honor to host Chris on the show.

References and Resources

https://wandb.ai/

https://www.youtube.com/c/WeightsBiases

https://x.com/weights_biases

https://www.linkedin.com/company/wandb/

https://twitter.com/vanpelt

Resources to learn more about Learning from Machine Learning

• https://www.youtube.com/@learningfrommachinelearning
• https://www.linkedin.com/company/learning-from-machine-learning
• https://mindfulmachines.substack.com/
• https://www.linkedin.com/in/sethplevine/
• https://medium.com/@levine.seth.p

This episode features Dr. Michelle Gill, Tech Lead and Applied Research Manager at NVIDIA, working on transformative projects like BioNemo to accelerate drug discovery through AI. Her team explores Biofoundation models to enable researchers to better perform tasks like protein folding and small molecule binding.

Michelle shares her incredible journey from wet lab biochemist to driving cutting edge AI at NVIDIA. Michelle discusses the overlap and differences between NLP and AI in biology. She outlines the critical need for better machine learning representations that capture the intricate dynamics of biology.

Michelle provides advice for beginners and early career professionals in the field of machine learning, emphasizing the importance of continuous learning and staying up to date with the latest tools and techniques. She also shares insights on building successful multidisciplinary teams

After hearing her fascinating PyData NYC keynote, it was such an honor to have her on the show to discuss innovations at the intersection of biochemistry and AI.

References and Resources

https://michellelynngill.com/

Michelle Gill - Keynote - PyData NYC https://www.youtube.com/watch?v=ATo2SzA1Pp4

AlexNet

AlphaFold - https://www.nature.com/articles/s41586-021-03819-2

OpenFold - https://www.biorxiv.org/content/10.1101/2022.11.20.517210v1

BioNemo - https://www.nvidia.com/en-us/clara/bionemo/

NeurIPS - https://nips.cc/

Art Palmer - https://www.biochem.cuimc.columbia.edu/profile/arthur-g-palmer-iii-phd

Patrick Loria - https://chem.yale.edu/faculty/j-patrick-loria

Scott Strobel - https://chem.yale.edu/faculty/scott-strobel

Alexander Rives - https://www.forbes.com/sites/kenrickcai/2023/08/25/evolutionaryscale-ai-biotech-startup-meta-researchers-funding/?sh=648f1a1140cf

Deborah Marks - https://sysbio.med.harvard.edu/debora-marks

Resources to learn more about Learning from Machine Learning

• https://www.linkedin.com/company/learning-from-machine-learning
• https://mindfulmachines.substack.com/
• https://www.linkedin.com/in/sethplevine/
• https://medium.com/@levine.seth.p

This episode features co-founder and CEO of Explosion, Ines Montani. Listen in as we discuss the evolution of the web and machine learning, the development of SpaCy, Natural Language Processing vs. Natural Language Understanding, the misconceptions of starting a software company, and so much more! Ines is a software developer working on Artificial Intelligence and Natural Language Processing technologies.

She's the co-founder and CEO of Explosion, the company behind SpaCy, one of the leading open-source libraries for NLP in Python and Prodigy, an annotation tool to help create training data for Machine Learning Models. Ines has an academic background in Communication Science, Media Studies and Linguistics and has been coding and designing websites since she was 11. She's been the keynote speaker at Python and Data Science conferences around the world.

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.

Listen on YouTube: https://youtu.be/XNFqFT-DZwo?si=Aj75TmsCyBQTyWqq

Listen on your favorite podcast platform:

https://rss.com/podcasts/learning-from-machine-learning/1190862/

References in the Episode

• https://explosion.ai/
• https://spacy.io/
• https://ines.io/
• Applied NLP Thinking
• Ines Montani - How to Ignore Most Startup Advice and Build a Decent Software Business Ines Montani: Incorporating LLMs into practical NLP workflows
• Ines Montani (spaCy) - Large Language Models from Prototype to Production [PyData Südwest] Confection
• https://github.com/explosion/confection

Resources to learn more about Learning from Machine Learning

• https://www.linkedin.com/company/learning-from-machine-learning
• https://mindfulmachines.substack.com/
• https://www.linkedin.com/in/sethplevine/
• https://medium.com/@levine.seth.p

This episode features Lewis Tunstall, machine learning engineer at Hugging Face and author of the best selling book Natural Language Processing with Transformers. He currently focuses on one of the hottest topic in NLP right now reinforcement learning from human feedback (RLHF). Lewis holds a PhD in quantum physics and his research has taken him around the world and into some of the most impactful projects including the Large Hadron Collider, the world's largest and most powerful particle accelerator. Lewis shares his unique story from Quantum Physicist to Data Scientist to Machine Learning Engineer.

Resources to learn more about Lewis Tunstall

• https://www.linkedin.com/in/lewis-tunstall/
• https://github.com/lewtun

References from the Episode

• https://www.fast.ai/
• https://jeremy.fast.ai/
• SetFit - https://arxiv.org/abs/2209.11055
• Proximal Policy Optimization
• InstructGPT
• RAFT Benchmark
• Bidirectional Language Models are Also Few-Shot Learners
• Nils Reimers - Sentence Transformers
• Jay Alammar - Illustrated Transformer
• Annotated Transformer
• Moshe Wasserblat, Intel, NLP, Research Manager
• Leandro von Werra, Co-Author of NLP with Transformers, Hugging Face Researcher
• LLMSys - https://lmsys.org/
• LoRA - Low-Rank Adaptation of Large Language Models

Resources to learn more about Learning from Machine Learning

• https://www.linkedin.com/company/learning-from-machine-learning
• https://mindfulmachines.substack.com/
• https://www.linkedin.com/in/sethplevine/
• https://medium.com/@levine.seth.p

The episode features Paige Bailey, the lead product manager for generative models at Google DeepMind. Paige's work has helped transform the way that people work and design software using the power of machine learning. Her current work is pushing the boundaries of innovation with Bard and the soon to be released Gemini.

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.

• Resources to learn more about Paige Bailey

  • https://twitter.com/DynamicWebPaige

  • https://github.com/dynamicwebpaige

• References from the Episode

  • Diamond Age - Neal Stephenson - https://amzn.to/3BCwk4n

  • Google Deepmind - https://www.deepmind.com/

  • Google Research - https://research.google/

  • Jax - https://jax.readthedocs.io/en/latest/

  • Jeff Dean - https://research.google/people/jeff/

  • Oriol Vinyals - https://research.google/people/OriolVinyals/

  • Roy Frostig - https://cs.stanford.edu/~rfrostig/

  • Matt Johnson - https://www.linkedin.com/in/matthewjamesjohnson/

  • Peter Hawkins - https://github.com/hawkinsp

  • Skye Wanderman-Milne - https://www.linkedin.com/in/skye-wanderman-milne-73887b29/

  • Yash Katariya - https://www.linkedin.com/in/yashkatariya/

  • Andrej Karpathy - https://karpathy.ai/

• Resources to learn more about Learning from Machine Learning

  • https://www.linkedin.com/company/learning-from-machine-learning

  • https://www.linkedin.com/in/sethplevine/

  • https://medium.com/@levine.seth.p