This repo contains notes I've taken while doing the MLOps specialization by DeepLearning.AI on Coursera.
The repo contains notes of the lectures as well as my own summaries of the references listed in the course. As I'm only done with the first course in the specialization, this repo is still work in progress.
I'll try to add a one-file summary (maybe in a blog post) when done with the whole specialization and all readings. It will also include my own point of view of what I get to see in industry + my discussions with people working in different fields.
Course 1: Introduction to Machine Learning in Production
-
Week 1
-
Week 1 readings summary: The links to all resources are listed within each markdown
-
Evidently AI blogs: Only one is listed in the course but I've read all the blog posts in ML monitoring series. They're good to let first week ideas soak in.
-
ChristopherGS: Monitoring Machine Learning Models in Production: It goes into further details and touches upon the subject of tools to use.
-
Challenges in Deploying Machine Learning: This article discusses problems faced by practitioners at each stage of the ML workflow. At the end of the article, the authors discuss some of the solutions that exist today.
-
Hidden Technical Debt in Machine Learning Systems: This article lists the causes of technical debt in ML systems and proposes ways of mitigating some of them.
-
-
Week 2
-
Week 3
Course 2: Machine Learning Data Lifecycle in Production
- Week 1