Chip Huyen is a Vietnamese-American computer scientist, author, and educator who turned a rejection letter from Stanford into a three-year around-the-world journey, two bestselling Vietnamese travel books, and eventually a second application that got her in. She went on to teach at Stanford, build ML infrastructure at NVIDIA and Netflix, co-found Claypot AI, and write two of the most-read technical books on machine learning systems in production - 'Designing Machine Learning Systems' (2022) and 'AI Engineering' (2025). Her newsletter and blog are required reading for anyone building serious AI products.
Josh Tobin is a machine learning infrastructure pioneer who spent three years as a research scientist at OpenAI - contributing to the famous Rubik's cube robot hand - before earning his PhD from UC Berkeley under Pieter Abbeel. He co-founded Gantry, an ML monitoring and continual learning startup that raised $28.3M, and created Full Stack Deep Learning, the first course focused on production ML engineering. His domain randomization technique, which transfers neural networks trained in simulation to the real world, has been cited over 600 times and reshaped how robotics teams build perception systems. He runs a newsletter focused on ML infrastructure and ops.
Mihail Eric is a Palo Alto-based ML engineer, researcher, educator, and serial founder who has spent a decade bridging cutting-edge AI research and production systems. A Stanford CS alumnus who studied under Christopher Manning and Percy Liang, he built some of Amazon Alexa's earliest large language models, co-founded YC-backed Storia AI, founded Confetti AI (acquired by Towards AI), and now teaches 'The Modern Software Developer' at Stanford while running a newsletter for 17,000+ AI practitioners.
Vicki Boykis is a founding ML engineer and one of the most respected voices in applied machine learning. Known for making complex systems legible through rigorous writing and dry wit, she runs the Normcore Tech newsletter, authored a widely-cited deep dive on embeddings, built Viberary (a semantic book recommendation engine), and created Normconf - an unconventional data conference celebrating the unglamorous realities of ML work. She brings an economist's skepticism and a software engineer's discipline to a field that often confuses hype for progress.
Weights & Biases (W&B) is the AI developer platform that the world's leading machine learning teams use to build, train, and deploy better models faster. Founded in 2017 in San Francisco, W&B provides experiment tracking, model management, and LLMOps tooling used by over 1 million developers - from OpenAI and Meta to Toyota and AstraZeneca. Acquired by CoreWeave in May 2025 for $1.7 billion, W&B is now the software backbone of one of the most important AI infrastructure companies in the world.
Weights & Biases (W&B) is the leading AI developer platform for machine learning and generative AI, offering tools for experiment tracking, hyperparameter optimization, model registry, and LLM application development. Founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis in San Francisco, W&B powers over 1 million developers and 1,400+ organizations — including OpenAI, Meta, and NVIDIA — by making it easier to build, train, evaluate, and deploy AI models. Acquired by CoreWeave for ~$1.7B in May 2025, W&B continues expanding its platform with Weave for LLM/agent observability, cementing its position as the de facto infrastructure for modern AI development.
Baseten is a San Francisco-based AI inference infrastructure company that provides dedicated and serverless GPU compute for running AI models at scale. Founded in 2019 by four ex-Gumroad engineers, the company has grown into a unicorn with a $5B valuation and $585M in total funding, backed by NVIDIA and other top-tier investors. Baseten powers inference workloads for 100+ enterprises including Cursor, Notion, HeyGen, and Clay, offering an inference stack with near-zero cold starts, proprietary networking, and open-source tooling like Truss for model packaging.
Modal (Modal Labs) is an AI-native serverless cloud computing platform that gives developers instant, elastic access to GPUs and CPUs through a clean Python SDK — no YAML, no Dockerfiles, no infrastructure management required. Founded in 2021 by Spotify ML veteran Erik Bernhardsson, Modal enables AI and ML teams to scale from zero to thousands of GPUs in seconds, paying only for what they use. With customers like Suno, Mistral AI, Harvey, Ramp, and Substack, Modal reached unicorn status at a $1.1B valuation in September 2025 and was reportedly in talks to raise at $2.5B just five months later.