Master practical MLOps workflows for deploying, tracking, and maintaining machine learning models in production. Learn to use MLflow, FastAPI, Docker, Airflow, and scikit-learn to automate and scale your ML projects.

Explore the foundations of MLOps, its lifecycle, and the essential tools that enable scalable, reliable machine learning workflows.

Learn to track, compare, and manage machine learning experiments and models using MLflow.

Deploy machine learning models as scalable APIs using FastAPI and Docker for real-world serving.

Automate and schedule end-to-end ML workflows using scikit-learn pipelines and Apache Airflow.

Monitor deployed models for drift, and implement CI/CD for robust, automated ML systems.

Challenge: Simulate Continuous Deployment