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

This episode we welcome Sebastian Raschka, Lead AI Educator at Lightning and author of Machine Learning with Pytorch and Scikit-Learn to discuss the best ways to learn machine learning, his open source work, how to use chatGPT, AGI, responsible AI and so much more. Sebastian is a fountain of knowledge and it was a pleasure to get his insights on this fast moving industry. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Sebastian Raschka and his work:

https://sebastianraschka.com/

https://lightning.ai/

Machine Learning with Pytorch and Scikit-Learn

Machine Learning Q and AI

Resources to learn more about Learning from Machine Learning and the host: https://www.linkedin.com/company/learning-from-machine-learning

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

https://medium.com/@levine.seth.p

twitter

References from Episode

https://scikit-learn.org/stable/

http://rasbt.github.io/mlxtend/

https://github.com/BioPandas/biopandas

Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch

Andrew Ng - https://www.andrewng.org/

Andrej Karpathy - https://karpathy.ai/

Paige Bailey - https://github.com/dynamicwebpaige

Contents

01:15 - Career Background

05:18 - Industry vs. Academia

08:18 - First Project in ML

15:04 - Open Source Projects Involvement

20:00 - Machine Learning: Q&AI

24:18 - ChatGPT as Brainstorm Assistant

25:38 - Hype vs. Reality

27:55 - AGI

31:00 - Use Cases for Generative Models

34:01 - Should the goal to be to replicate human intelligence?

39:18 - Delegating Tasks using LLM

42:26 - ML Models are overconfident on Out of Distribution

44:54 - Responsible AI and ML

45:59 - Complexity of ML Systems

47:26 - Trend for ML Practitioners to move to AI Ethics

49:27 - What advice would you give to someone just starting out?

52:20 - Advice that you’ve received that has helped you

54:08 - Andrew Ng Advice

55:20 - Exercise of Implementing Algorithms from Scratch

59:00 - Who else has influenced you?

01:01:18 - Production and Real-World Applications - Don’t reinvent the wheel

01:03:00 - What has a career in ML taught you about life?

This episode welcomes Nils Reimers, Director of Machine Learning at Cohere and former research at Hugging Face, to discuss Natural Language Processing, Sentence Transformers and the future of Machine Learning. Nils is best known as the creator of Sentence Transformers, a powerful framework for generating high-quality sentence embeddings that has become increasingly popular in the ML community with over 9K stars on Github. With Sentence Transformers, Nils has enabled researchers and developers (including me) to train state-of-the-art models for a wide range of NLP tasks, including text classification, semantic similarity, and question-answering. His contributions have been recognized by numerous awards and publications in top-tier conferences and journals.

Resources to learn more about Nils Reimers and his work:

https://www.nils-reimers.de/

https://www.sbert.net/

https://scholar.google.com/citations?...

https://cohere.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

Contents

02:29 What attracted you to Machine Learning?

06:32 What is sentence transformers?

28:02 Benchmarks and P-Hacking

33:53 What’s an important question that remains unanswered in Machine Learning?

38:41 How do you view the gap between the hype and the reality in Machine Learning?

50:45 What advice would you give to someone just starting out?

52:30 What advice would you give yourself when you were just starting out in your career?

57:22 What has a career in ML taught you about life?

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. This episode we welcome Vincent Warmerdam, creator of calmcode, and machine learning engineer at SpaCy to discuss Data Science, models and much more. @learningfrommachinelearning

Resources to learn more about Vincent Warmerdam:

https://calmcode.io/

https://youtu.be/kYMfE9u-lMo

https://youtu.be/S7vhi6RjBZA

https://github.com/koaning

References from the Episode:

You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place https://amzn.to/3Jt1qjX

The Future of Operational Research is Past https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdf

Supervised Learning is great - it's data collection that's broken https://explosion.ai/blog/supervised-learning-data-collection

Deon - An ethics checklist for data scientists https://deon.drivendata.org/

Hadley Wickham - https://hadley.nz/

Katharine Jarmul - https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=de

Vicki Boykis - https://vickiboykis.com/

Brett Victor - https://youtu.be/8pTEmbeENF4

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