Lilly Tech Systems
MLflow Certification
A complete exam prep course for the Databricks MLflow certification. This course covers every major topic — MLflow Tracking, Models & Registry, Projects & Recipes, and Model Deployment — with practice questions and hands-on exercises that mirror the actual exam format.
Your Learning Path
Follow these lessons in order for complete Databricks MLflow certification preparation, or jump to any topic area.
1. Exam Overview
Exam format, topics covered, cost, registration process, and a recommended study plan to pass the Databricks MLflow certification on your first attempt.
2. MLflow Tracking
Experiments, runs, parameters, metrics, artifacts, autologging, search API, and practice questions covering the Tracking component.
3. MLflow Models & Registry
Model flavors, model signature, input examples, the Model Registry, stage transitions, and practice questions for model management.
4. MLflow Projects & Recipes
MLproject files, conda and Docker environments, entry points, MLflow Recipes for common ML tasks, and practice questions.
5. Model Deployment with MLflow
Serving models locally, REST API endpoints, batch scoring, Docker containers, cloud deployment on AWS/Azure/GCP, and practice questions.
6. Exam Tips & Practice
Quick reference cheat sheet, full-length practice questions, common exam mistakes to avoid, and a comprehensive FAQ accordion.
What You'll Learn
By the end of this course, you will be able to:
Track Experiments
Log parameters, metrics, and artifacts using the MLflow Tracking API. Compare runs, search experiments, and use autologging for popular frameworks.
Manage Models
Register models in the MLflow Model Registry, transition between stages (Staging, Production, Archived), and version models with lineage tracking.
Package & Reproduce
Create reproducible ML projects with MLproject files, manage environments with conda/Docker, and use MLflow Recipes for standard workflows.
Deploy Models
Serve models as REST APIs, perform batch scoring, containerize with Docker, and deploy to cloud platforms for production inference.