MLOps Engineering: From Models to Production
A hands-on, project-based course that takes you from containerizing ML workloads to building fully automated, monitored, and cloud-deployed machine learning pipelines. You'll master Docker, Kubernetes, CI/CD, MLflow, infrastructure as code, and production monitoring while building a portfolio of deployable systems.
16 lessons · 4 modules
Module 1
Containerization & Foundations for ML Workloads
Module 2
Experiment Tracking, Pipelines & Model Deployment
Module 3
Orchestration, CI/CD & Infrastructure as Code
Module 4