MLOps: Machine Learning in Production

Build the infrastructure and workflows to reliably train, deploy, monitor, and iterate on machine learning models at scale.

What You’ll Learn

• ML pipelines with Kubeflow, Airflow, and Prefect
• Experiment tracking with MLflow and Weights & Biases
• Model versioning, registry, and CI/CD for ML
• Containerizing ML workloads with Docker and Kubernetes
• Feature stores and data versioning with DVC
• Monitoring model performance and data drift in production