This complete MLflow and Databricks course is designed for machine learning engineers who want to master modern MLOps and LLMOps practices. The course covers the entire machine learning lifecycle, from local experimentation and model tracking to enterprise-scale deployment and monitoring.

You will start by learning the fundamentals of MLOps, including why machine learning systems require experiment tracking, reproducibility, and proper lifecycle management. The course then introduces MLflow, teaching how to track experiments, log metrics, manage artifacts, and organize machine learning projects efficiently.

As you progress, you will explore advanced model management concepts such as model versioning, registries, aliases, and deployment through production-ready endpoints. The course also dives into Generative AI operations (LLMOps), covering prompt registries, prompt versioning, evaluation frameworks, and OpenAI integration.

A major section focuses on advanced LLM evaluation techniques, including custom scorers, business-specific evaluation logic, AI-generated rationale analysis, and performance visualization.

The enterprise portion of the course demonstrates how MLflow integrates with Databricks, including serverless compute, Unity Catalog, collaboration workflows, centralized model registries, and secure model serving.

Finally, a real-world project shows how to deploy Hugging Face Transformer models using custom MLflow workflows and Databricks infrastructure. By the end, you will understand how to build scalable, reproducible, and enterprise-ready machine learning and AI systems