MLflow Mastery: End-to-End MLOps with MLflow with Udaykumar Gajavalli

Master MLflow across the complete Machine Learning lifecycle — from your first experiment to production deployment and GenAI observability.

This course is designed for Data Scientists, Machine Learning Engineers, AI Engineers, and aspiring MLOps Engineers who want to understand how MLflow is used in real-world ML and AI systems.

Instead of learning MLflow as just an experiment-tracking tool, you will understand how its different capabilities fit together across the complete ML lifecycle.

The course progresses from MLflow foundations into experiment tracking, reproducible projects, model packaging, model registry, deployment, evaluation, GenAI/LLM tracing, integrations, and production MLOps practices. The curriculum concludes with an end-to-end project covering the workflow from train → track → register → evaluate → deploy → trace.

What you will learn:

• MLflow architecture and core concepts
• Experiments, runs, parameters, metrics, artifacts, and tags
• MLflow autologging and hyperparameter tuning
• Reproducible MLflow Projects
• MLflow Models, flavors, PyFunc, and model signatures
• Model Registry, versioning, aliases, lineage, and governance
• Batch and real-time model deployment
• ML model and LLM evaluation
• LLM, RAG, and Agent tracing with MLflow
• Prompt versioning and token/cost tracking
• Databricks, Azure ML, Spark, DVC, Optuna, and CI/CD integrations
• Production MLOps practices, monitoring, reproducibility, and model drift
• End-to-end hands-on MLflow project

Your GenAI section is particularly useful for differentiating the course: the curriculum includes LLM tracing, OpenAI/LangChain/LlamaIndex auto-tracing, RAG tracing, agent tracing, prompt versioning, token usage, and MLflow 3.0 concepts.

Each chapter is accompanied by a detailed PDF, allowing learners to use the course material both during learning and later as an MLflow reference guide.

By the end of the course, learners should understand not just individual MLflow commands, but where MLflow fits within an end-to-end production ML/AI architecture and how its components work together.