Syllabus¶

What you will learn from this guide.

• Introduction - Learn about the concept behind MLOps.
• Part 1 - Local training - Learn how to train a model locally using DVC.
  • Chapter 1.1 - Run a simple ML experiment with Jupyter Notebook
  • Chapter 1.2 - Adapt and move the Jupyter Notebook to Python scripts
  • Chapter 1.3 - Initialize Git and DVC for local training
• Part 2 - Model evaluation - Learn how to evaluate a model using DVC.
  • Chapter 2.1 - Reproduce the ML experiment with DVC
  • Chapter 2.2 - Track model evolution with DVC
• Part 3 - Serve the model - Learn how to serve the model using BentoML and Docker.
  • Chapter 3.1 - Save and load the model with BentoML
  • Chapter 3.2 - Serve the model locally with BentoML
  • Chapter 3.3 - Build and publish the model with BentoML and Docker locally
• Conclusion - Summary of what you have done and what is left to be done.