Data Engineering & MLOps
This section is aimed at data scientists wanting to get familiar with data engineering & MLOps. Resources are meant to be introductory and not a full representation of the entire fields.
For an extensive list of data engineering resources, refer to Reddit's Data Engineering Community.
For an extensive list of MLOps resources, refer to Awesome MLOps.
Books
Designing Machine Learning Systems | Learn a holistic approach to designing ML systems | Chip Huyen
Programming
Title | Description & Context | Source |
---|---|---|
What every programmer absolutely, positively needs to know about encodings and character sets to work with text | Everything you need to learn about encodings | |
Python Practice Problems for Beginner Coders | Practice problems for people that find Leetcode/Hackerrank too complicated | Berkely |
Git
Title | Description & Context | Source |
---|---|---|
Introduction to Git | Good beginner blogpost on the basics of Git | Made with ML |
How to Contribute to an Open Source Project on GitHub | Videos detailing the process of contributing to an Open Source project on Github | Egg Head |
git exercises: navigate a repository | Exercises to help you learn Git | Julia Evans |
Jupyter Notebooks
Title | Description & Context | Source |
---|---|---|
Jupyter Notebook Tips and Improvements | Notebook extensions and hotkeys to work more efficiently | Beginner |
Packaging
Title | Description & Context | Source |
---|---|---|
Packaging a Python Codebase | Guide on how to package your code with Python | Made with ML |
Testing
Title | Description & Context | Source |
---|---|---|
Testing in Python | Learn how to make unit & integration tests for your code | Real Python |
Docker
Title | Description & Context | Source |
---|---|---|
Docker for data scientists — Part 1 | Blog series about creating a first project with Docker | towards data science |
SQL
Title | Description & Context | Source |
---|---|---|
How To Create a SQL Practice Database with Python | Practice creating & querying an SQL database with fake data | towards data science |
Don’t Make These 5 Mistakes with SQL | Tips and examples on common SQL features | Intermediate |