Core
Specializations
ML Engineering
MLE Courses
Prerequisites: Intro CS & Coding.
Recommended path
- The Full Stack Deep Learning bootcamp is a must for anyone looking to up their ML engineering game. The lectures cover ML project management, infrastructure & tooling, model debugging, data management, testing, explainability, deployment and monitoring of ML models. They also have interactive exercises/labs that teach you skills such as tracking your experiments or labeling your data. If you're already familiar with basic deep learning, you can skip ahead to lecture 5 and lab 5.
Course
Year
Description
Difficulty Level
Resources
2021
A treasure trove of lectures that will teach you practical lessons how to become a better ML practitioner. Whether it's about setting up your ML projects, managing experiments or deploying ML models, FSDL got you covered.
Easy -Medium
Supplementary Resources
Resource
Year
Description
Type
2021
This course unfortunately does not have recorded lectures but the syllabus has some really useful notes and slides, e.g. tons of material on data engineering.
📚
2019
Chip (the instructor for CS329) wrote this handy booklet that covers four main areas: project setup, data pipeline, modeling and serving. It also includes some neat exercises and pointers to cases studies.
📚
2019
Likely the most well-known blog post on model training and debugging with some really useful tips on how to approach training neural networks methodically.
🌐
2018
Not super relevant for day-to-day ML engineering operations but an interesting course nonetheless. It has lots of case studies and historical reviews about scaling computational experiments.
🎥
Continuously updated
An awesome collection of talks on topics within ML Systems. For example, on debugging ML in production and principles of good ML systems design.
🎥
Last modified 1yr ago
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