This three-course specialization is built for ML practitioners and software engineers who want to stop experimenting and start shipping. You will master the engineering practices required to take trained models from notebooks to production — focusing on DevOps automation, cloud deployment, and containerized serving rather than model theory. Starting with DevOps foundations, you will build automated ML training pipelines with GitHub Actions, serve models through FastAPI, and implement CI/CD workflows from code to deployment using Docker.
As you progress, you will gain a comprehensive understanding of cloud ML platforms across AWS, Azure, and GCP — learning when to use SageMaker, Vertex AI, or Azure ML Studio, and how to evaluate build-vs-buy decisions for managed ML services. The final course takes you deep into production model serving — building Dockerized ML services from scratch, designing multi-model serving APIs with versioning and A/B testing, optimizing prediction latency, and implementing batch and real-time inference patterns. By the end, you will have the engineering toolkit to reliably ship, serve, and scale ML models across any deployment environment.
