PRODUCTION

MLOps

Bridge the gap between model development and production by implementing robust machine learning operations.

~35 hours

Learning Objectives

Model Containerization (Docker)Model DeploymentMLflowModel MonitoringCI/CD for ML

Containerize a Scikit-Learn model using Docker and deploy it as a REST API using FastAPI.

📖 Learn by Reading

Designing Machine Learning Systems

Chip Huyen

A must-read for architecting reliable, scalable, and maintainable ML systems in production.

🎥 Learn by Watching

Machine Learning Engineering for Production (MLOps)

DeepLearning.AI • Coursera • 60:00:00

Covers the entire lifecycle of ML in production, from deploying models to monitoring concept drift.