Add Learning & Educational Resources section

[alibad]

Proposing New Section: Learning & Educational Resources

Rationale

This repository has excellent coverage of tools for production ML, but lacks resources to learn how to use them effectively.

Adding a "Learning & Educational Resources" section would:

• Help newcomers navigate the steep learning curve of MLOps
• Provide context for when/why to use the listed tools
• Complement the existing tool-focused structure
• Support practitioners upskilling on production ML

Section Placement

Added before the Contributing section, following the pattern of other major sections.

Initial Entry: System Designer

Starting with one high-quality educational platform:

System Designer - ML Systems (https://systemdesigner.net/ml-systems)

Why this resource:

• Production-focused: 28 lessons specifically on deploying ML to production
• Covers tools in this list: Teaches when to use TensorFlow Serving, feature stores, monitoring tools, etc.
• Interactive learning: AI tutor, whiteboards, quizzes, hands-on projects
• Free and accessible: No signup or paywall
• Complements this repo: Helps people understand why and how to use the tools listed here

Coverage:

• Model deployment and serving
• Feature stores and engineering
• ML monitoring and drift detection
• Training orchestration
• Production ML best practices

Future Additions

This section could grow to include:

• MLOps courses (Coursera, Udacity)
• Official documentation guides
• Case studies and blog posts
• Research papers on production ML
• Community tutorials

Open to Feedback

Happy to:

• Adjust section placement
• Change section naming
• Add more initial resources
• Modify formatting

Let me know your thoughts on adding a learning resources section to this list!