On today’s episode of The Pragmatic Engineer, I’m joined by Chip Huyen, a computer scientist, author of the freshly published O’Reilly book AI Engineering, and an expert in applied machine learning.
New models will keep coming up from same vendor so it’s important to have model agnostic Integration tests for the API use cases to upgrade models smoothly
This was an insightful episode! Chip Huyen’s expertise in AI Engineering sheds light on the practical aspects of applied machine learning, especially the differences between AI and ML engineering. The discussion on fine-tuning and its limited necessity was particularly interesting. Also, the take on project-based learning combined with structured learning is a valuable perspective for anyone diving into AI. Looking forward to exploring more about AI's role in education and entertainment!
Chip Huyen is a great contributor to ML Engineering and MLOps especially. But beeing honest it was hard to follow her, she is much better in writing. I might be also just not smart enough.
This is a very informative and insightful episode. I really appreciated how she broke down the differences between AI engineers, software engineers, and ML engineers. Those distinctions were blurry for me, so her clarification helps set realistic expectations about the skills needed.
Then her point about combining project-based work with structured learning is spot-on. That balance is crucial. Theoretical knowledge without practical application can be hard to master, while purely project-based learning might miss important fundamentals. This hybrid approach helps us be more well-rounded, as understanding the theory and implementing effectively.
New models will keep coming up from same vendor so it’s important to have model agnostic Integration tests for the API use cases to upgrade models smoothly
This was an insightful episode! Chip Huyen’s expertise in AI Engineering sheds light on the practical aspects of applied machine learning, especially the differences between AI and ML engineering. The discussion on fine-tuning and its limited necessity was particularly interesting. Also, the take on project-based learning combined with structured learning is a valuable perspective for anyone diving into AI. Looking forward to exploring more about AI's role in education and entertainment!
Chip Huyen is a great contributor to ML Engineering and MLOps especially. But beeing honest it was hard to follow her, she is much better in writing. I might be also just not smart enough.
This is a very informative and insightful episode. I really appreciated how she broke down the differences between AI engineers, software engineers, and ML engineers. Those distinctions were blurry for me, so her clarification helps set realistic expectations about the skills needed.
Then her point about combining project-based work with structured learning is spot-on. That balance is crucial. Theoretical knowledge without practical application can be hard to master, while purely project-based learning might miss important fundamentals. This hybrid approach helps us be more well-rounded, as understanding the theory and implementing effectively.