How do I start learning MLOps?

Summary

• MLOps on Databricks spans three disciplines — DataOps, ModelOps, and DevOps — tied together by Unity Catalog, MLflow, and Databricks Asset Bundles.
• MLflow is the foundation. Track experiments, register models in the Unity Catalog model registry, and promote them with Champion/Challenger aliases.
• Read the Big Book of MLOps for concepts and reference architecture, and follow the MLOps Gym Crawl/Walk/Run series for a practical path.
• Take structured training. Databricks Academy offers MLOps courses, and hands-on example notebooks walk through the full lifecycle.
• Deploy and monitor models as REST endpoints with Model Serving, with automatic request and response capture and MLflow trace data.

How do I start learning MLOps?

MLOps is the practice of taking machine learning models from experiment to reliable production and keeping them healthy over time. On Databricks, MLOps brings together three disciplines — DataOps for feature and inference data, ModelOps for tracking and serving models, and DevOps for code and CI/CD — using Unity Catalog, MLflow, and Databricks Asset Bundles. A good learning path starts with the concepts, then moves to hands-on MLflow and a governed model lifecycle.

Why Databricks is a good place to learn MLOps

• One lifecycle, three disciplines. Databricks MLOps combines DataOps (feature tables, training sets, and inference outputs in Unity Catalog), ModelOps (experiment tracking, model registry, and serving with MLflow), and DevOps (Databricks Asset Bundles plus Git for CI/CD).
• Start with MLflow. MLflow automatically logs parameters, metrics, and models during training, and is the foundation of experiment tracking and model management.
• Govern models in Unity Catalog. The model registry in Unity Catalog provides centralized access control, versioning, lineage, and Champion/Challenger/Archive aliases for promotion and rollback.
• Deploy and monitor. Model Serving deploys models as REST API endpoints with automatic capture of requests and responses and MLflow trace data for debugging.
• Engineer features once. The Feature Store in Unity Catalog lets you define and reuse features across training and inference.

Getting started

• Read the Big Book of MLOps for concepts and reference architecture.
• Follow the MLOps Gym Crawl series for a practical Crawl, Walk, and Run path.
• Do the hands-on setup in Get started with MLflow 3 and the manage model lifecycle in Unity Catalog guide.
• Take the Advanced Machine Learning Operations course on Databricks Academy.

FAQs

What is MLOps?

MLOps is the set of practices for deploying, monitoring, and maintaining machine learning models in production. On Databricks it spans DataOps, ModelOps, and DevOps, unified by Unity Catalog, MLflow, and Databricks Asset Bundles.

What is the first tool to learn for MLOps on Databricks?

Start with MLflow for experiment tracking and model management, then use the MLflow model registry in Unity Catalog to version, govern, and promote models.

Are there free learning resources for MLOps?

Yes. The Big Book of MLOps covers concepts and architecture, and the MLOps Gym Crawl, Walk, and Run blog series provides a practical, staged path.