This MLOps with Databricks course provides a complete introduction to building and deploying machine learning systems in production using modern MLOps practices. It focuses on how to manage the full machine learning lifecycle using Databricks and MLflow, from development to deployment.
The course begins with an introduction to MLOps concepts and explains how machine learning workflows differ when moving from research to production environments. You will then learn how to develop and manage ML projects inside Databricks, a widely used platform for scalable data and AI workflows.
A key part of the course is working with MLflow, where you will learn how to track experiments, log model metrics, and manage different versions of trained models. You will also explore how to register models properly so they can be reused and deployed efficiently.
The course continues with model serving architectures, showing how machine learning models are delivered as scalable APIs or endpoints. You will also learn how to deploy model serving endpoints using Databricks, enabling real-time inference in production systems.
By the end of this course, you will understand how to build end-to-end MLOps pipelines using Databricks and MLflow, and how to deploy reliable, scalable machine learning models in real-world production environments.
