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