Applied ML in Production

A guide and code-driven case study on MLOps for software engineers, data scientists and product managers. We will be developing an end-to-end ML feature, from product → ML → production, with open source tools.


📦 Product 🔢 Data 🤖 Modeling
  • Random
  • Rule-based
  • Sklearn
  • CNN
  • Transformers
📝 Scripting
  • OOPs
  • Packaging
  • Logging
  • Testing (basics)
  • Testing (ML)
  • Formatting
  • Makefile
  • Precommit
  • Git
🎛 Tuning
  • Experiment tracking
  • Optimization
  • RESTful API
  • Databases
  • Authentication
  • Documentation
🚀 Production
  • Dashboard
  • Docker
  • Serving
  • Monitoring (performance, drift)
  • CI/CD (GitHub actions)
  • Active learning
  • Scaling


Who is this course for?

What is the structure?

Lessons will be released weekly and each one will include:

What are the prerequisites?

You should have some familiarity with Python and basic ML algorithms. While we will be experimenting with complex model architectures, you can easily apply the lessons to any class of ML models.

What makes this course unique?

Who is the author?

Why is this free?

This is especially targeted for people who don’t have as much opportunity around the world. I firmly believe that creativity and intelligence are randomly distributed but opportunity is siloed. I want to enable more people to create and contribute to innovation.

Return to courses

[ applied-ml mlops production ]