Data scientists at the start of their careers often have the misconception that their job will be largely focused on training and improving models in a Jupyter notebook. The reality is that most data science value is created outside of a notebook.
This crash course is intended for data scientists with basic knowledge of developing machine learning models in a Jupyter notebook setting. By the end of the session, you will know:

1. What is MLOps (Machine Learning Operationalization) and why it’s necessary?
2. What is a machine learning pipeline?
3. How to create and deploy a fully reproducible MLOps pipeline from scratch.
4. Learn the basics of continuous training, drift detection, alerts, and model deployment.