A machine learning podcast that explores more than just algorithms and data: Life lessons from the experts. Welcome to "Learning from Machine Learning," a podcast about the insights gained from a career in the field of Machine Learning and Data Science. In each episode, industry experts, entrepreneurs and practitioners will share their experiences and advice on what it takes to succeed in this rapidly-evolving field.
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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