Summary
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.
In this ever-evolving and uncertain world of entrepreneurship and machine learning, it's easy to get caught up in the break-neck pace of innovation and advancement. However, we must never lose sight of the humans who are tackling these challenges. poignantly explains the importance of embracing the human element and fostering an environment of respect, understanding and empathy.
While we harness the power of technology and move into a future where AI is integrated into more of our lives, we must nurture the essence of what makes us human.
Quotes
1️⃣ "My title is co-founder really at the end of the day. And that's one of the things I love most about the job is that I'll get brought into anything at any time and can be really versatile and just try to solve problems pragmatically."
2️⃣ "These models are going to get better. They're going to do more amazing things. It's an exciting time for us to be in. But as these models get generally better, this problem of, all right, well, when it fails, knowing how it fails and doing everything we can to inform the user and protect against it, it's going to become even bigger. Because we're going to start trusting these things more."
3️⃣ "It's tempting, you can make a cool demo today. It's been so fun as an engineer having access to this technology and to delight myself when I make something and I'm like, "wow, it did that. I can't believe it did it." But that demo where you then kind of script it and you're showing your friends this cool thing you made, it does not account for all of the weird edge cases and things you haven't thought about and ways in which another user is going to interact with this thing."
References and Resources
Resources to learn more about Learning from Machine Learning
Contents
00:00 Opening
00:42 Introduction and Background
03:27 Full-Stack Engineer
04:57 Introduction to Weights and Biases
08:51 Evolution of Weights and Biases
13:25 Unique Uses of Weights and Biases
15:36 Future of Weights and Biases
22:14 Evaluation in Machine Learning
29:48 Hype and Reality in Machine Learning
35:40 The Gap Between Hype and Reality
36:51 Challenges in Operationalizing AI Models
38:50 Hardware for AI Systems
39:37 The Complexity of Scaling AI Models
40:20 Unanswered Questions in Machine Learning
42:03 Multimodal Work in Machine Learning
42:47 The Quest for AGI
46:14 Lessons from Entrepreneurship in Machine Learning
50:06 Setting a Price
57:46 Role is Co-Founder
59:08 Advice you wish you received
01:00:26 What has a career in ML and entrepreneurship taught you about life?
01:01:58 Joy of Problem Solving and Creating
01:02:50 Learn more about W&B and CVP
Share this post