This course provides a hands-on introduction to MLOps using Databricks, focusing on how to build, track, deploy, and monitor machine learning models in real production environments.
The course begins with an overview of MLOps and introduces the Databricks platform, explaining how it supports end-to-end machine learning workflows. You will learn how to develop within Databricks notebooks and set up a scalable ML development environment.
A major focus of the course is MLflow, where you will learn how to log experiments, track model performance, and register models for production use. This is essential for building reproducible and well-managed ML systems.
The course then moves into model serving and deployment, showing how to create scalable endpoints for real-time inference. You will also explore Databricks Asset Bundles, which help manage and organize ML projects efficiently.
In addition, the course covers CI/CD strategies for machine learning, helping you automate testing and deployment pipelines. The final section introduces monitoring systems, teaching how to track model performance and detect issues after deployment.
By the end of this course, you will understand the full MLOps lifecycle using Databricks—from development and training to deployment and monitoring—preparing you for real-world production AI systems.
