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.
Projet d'apprentissage appliqué
Throughout the specialization, learners complete hands-on engineering projects focused on building real deployment infrastructure. You will train a classification model with automated pipelines using GitHub Actions, build and deploy a FastAPI ML service with CI/CD and automated testing, and package models in Docker containers with all dependencies.
Learners deploy the same model across AWS SageMaker, Azure ML Studio, and GCP Vertex AI to compare managed platform workflows and cost trade-offs. The final projects challenge learners to containerize ML models using Docker best practices, build multi-model serving APIs with version control and A/B testing capabilities, create batch inference pipelines, and optimize prediction latency using ONNX model serialization and input validation strategies — ensuring all skills translate directly to production ML engineering roles.
