Practical Machine Learning on Databricks

Table of Contents

1. ML Process and Challenges
2. Overview of ML on Databricks
3. Utilizing Feature Store
4. Understanding MLflow Components
5. Create a Baseline Model for Bank Customer Churn Prediction Using AutoML
6. Model Versioning and Webhooks
7. Model Deployment Approaches
8. Automating ML Workflows Using the Databricks Jobs
9. Model Drift Detection for Our Churn Prediction Model and Retraining
10. CI/CD to Automate Model Retraining and Re-Deployment.