Syllabus¶

What you will learn from this guide.

• Introduction - Learn about the concept behind MLOps and the tools used in this guide.
• Part 1 - Local training and model evaluation - Learn how to train a model locally and evaluate it 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
  • Chapter 1.4 - Reproduce the ML experiment with DVC
  • Chapter 1.5 - Track model evolution with DVC
• Part 2 - Move the model to the cloud - Learn how to collaborate online using Git, a CI/CD pipeline and CML.
  • Chapter 2.1 - Move the ML experiment data to the cloud
  • Chapter 2.2 - Move the ML experiment code to the cloud
  • Chapter 2.3 - Reproduce the ML experiment in a CI/CD pipeline
  • Chapter 2.4 - Track model evolution in the CI/CD pipeline with CML
  • Chapter 2.5 - Work efficiently and collaboratively with Git
• Part 3 - Serve and deploy the model - Learn how to serve and deploy 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
  • Chapter 3.4 - Build and publish the model with BentoML and Docker
  • Chapter 3.5 - Deploy and access the model on Kubernetes
  • Chapter 3.6 - Continuous deployment of the model with the CI/CD pipeline
  • Chapter 3.7 - Use a self-hosted runner for the CI/CD pipeline
  • Chapter 3.8 - Train the model on a Kubernetes pod
• Part 4 - Labeling the data and retrain the model - Learn how to label new data and retrain the model using Label Studio.
  • Chapter 4.1 - Setup Label Studio
  • Chapter 4.2 - Label new data with Label Studio
  • Chapter 4.3 - Link the model to Label Studio
  • Chapter 4.4 - Retrain the model from new data with DVC
• Conclusion - Summary of what you have done and what is left to be done.

You can find this guide on GitHub. Don't hesitate to show your support by giving us a star or opening an issue if you encounter any difficulties. :)