MLOps with Databricks: Machine Learning End-to-End

Maria Vechtomova guides you through MLOps principles and explains how Databricks handles the machine learning lifecycle holistically, from data preparation to model deployment and monitoring, and enables data engineers, data scientists, and MLOps engineers to collaborate seamlessly. To put all the pieces together, you’ll navigate two ML projects: a real-time ML application and a RAG system that highlights LLM-specific Databricks features.

Understand the Databricks components for MLOps
Unpack ML model serving architectures
Track your machine learning experiments and register your models
Build an ML application that uses feature and model serving, and model serving with automatic feature lookup
Deploy a real-time ML application and a RAG application
Use Databricks to monitor ML applications for data and model drift