Master MLflow + Databricks in Just 5 Hours — Complete Beginner to Advanced Guide
Want to build production-ready ML systems on Databricks using MLflow? In this deep-dive 5-hour masterclass, I walk you step-by-step through real-world workflows — from experiment tracking to model deployment — all inside the Databricks ecosystem.
If you're serious about becoming an ML Engineer, MLOps Engineer, or Data Scientist working on large-scale ML systems, this video is for you.
🚀 What You’ll Learn
✅ What MLflow is and why it matters in real-world ML systems
✅ Setting up MLflow inside Databricks
✅ Experiment Tracking (parameters, metrics, artifacts)
✅ Model Logging & Versioning
✅ MLflow Model Registry
✅ Reproducible ML Pipelines
✅ Hyperparameter tuning with tracking
✅ Serving models in Databricks
✅ Production-grade ML workflow architecture
🧠 Who Is This For?
• Data Scientists wanting to move into production ML
• ML Engineers learning MLOps foundations
• Databricks users who want structured ML experimentation
• Anyone preparing for ML Engineering interviews
• Developers building scalable ML systems
💡 Why MLflow + Databricks?
In modern ML systems, training a model is only 20% of the work. The real value comes from:
Reproducibility
Experiment comparison
Model version control
Deployment workflows
Governance
MLflow + Databricks together give you a complete lifecycle management platform for machine learning.
🛠 Tools Covered
• Databricks Workspace
• MLflow Tracking
• Model Registry
• Python (scikit-learn / PySpark ML optional)
• Real ML use-case example
Slides: https://github.com/datageekrj/MLflow-...