Machine learning systems design
Design a machine learning system Designing a machine learning system is an iterative process. There are generally four main components of the process: project setup, data pipeline, modeling (selecting, training, and debugging your model), and serving (testing, deploying, maintaining). The output from one step might be used to update the previous steps. Some scenarios: After examining the available
What we look for in a resume
I read every single one of the resumes we receive. Sometimes, I’d talk to a candidate and see that what we perceived as their strongest aspects actually weren’t included in their resume. Occasionally, a candidate would tell me that they didn’t expect their resume to still be screened by humans – had they known, they would have written their resume differently. The resume evaluation process is pret
MLOps guide
A collection of materials from introductory to advanced. This is roughly the path I’d follow if I were to start my MLOps journey again. Table of contents ML + engineering fundamentals MLOps …. Overview …. Intermediate …. Advanced Career Case studies Bonus ML + engineering fundamentals While it’s tempting to want to get straight to ChatGPT, it’s important to have a good grasp of machine learning, d
Building LLM applications for production
[Hacker News discussion, LinkedIn discussion, Twitter thread] A question that I’ve been asked a lot recently is how large language models (LLMs) will change machine learning workflows. After working with several companies who are working with LLM applications and personally going down a rabbit hole building my applications, I realized two things: It’s easy to make something cool with LLMs, but ver
Data Distribution Shifts and Monitoring
Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). For the fully developed text, see the book Designing Machine Learning Systems (Chip Huyen, O’Reilly 2022). Slides (much shorter 😁). Original Google Docs version. Let’s start the note with a story I was told by an executive that many readers might be able to relate to. About two
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Introduction to Machine Learning Interviews Book · MLIB
Self-serve feature platforms: architectures and APIs
