#AIengineering #MachineLearning #FoundationModels Chip Huyen joins host @JonKrohnLearns for this milestone episode 999 to talk about her record-breaking book "AI Engineering" the most-read title on the O'Reilly platform last year and how the AI landscape has shifted since her last appearance. Chip breaks down what separates AI engineering from machine learning engineering, makes the case for a "start simple" workflow, gets candid about the real costs of running LLMs in production, and shares why she's now fascinated by physical AI, robotics, and world models and why the durable problems worth solving are increasingly human ones. Jon Krohn guides the conversation from the practical content of the book through to where the field is heading next. This episode is brought to you by: • Cisco: https://outshift.cisco.com • Acceldata: https://www.acceldata.io Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:06:48) What separates AI engineering from machine learning engineering • (00:14:44) The “start simple” approach: prompting, then RAG, then fine-tuning • (00:18:19) Why web search is so painfully expensive in production • (00:35:21) Is the “ChatGPT moment” for physical AI really here? • (00:53:08) Why the durable problems left to solve are people problems Additional materials: https://www.superdatascience.com/999