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
Full Stack Deep Learning -Bootcamp​
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
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Made with ML - MLOps​
Continuously updated
One of the coolest projects to come out of the ML education space in recent years. This course is similar to a blog with a ton of invaluable code exercises. There's also video material for the first module of the course.
Medium
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Supplementary Resources
Resource
Year
Description
Type
Stanford - CS329S​
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.
πŸ“š
Chip Huyen - ML Systems Design​
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.
πŸ“š
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.
πŸŽ₯
Stanford - MLSys Seminars​
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.
πŸŽ₯
Last modified 5mo ago
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