This specialization empowers software engineers, backend developers, and full-stack professionals to integrate, deploy, and maintain machine learning models within production software systems. You will approach ML through an engineering lens — emphasizing software design, APIs, scalability, and maintainability rather than theory alone. Starting with applied ML fundamentals, you will build and train models using Scikit-learn, TensorFlow, and PyTorch while writing modular, testable ML code.
As you progress, you will convert ML models into production-ready APIs using FastAPI and Flask, design scalable microservices for inference, and manage model versioning and performance optimization. The third course introduces MLOps foundations — covering reproducibility, experiment tracking, and version control using Git, DVC, and MLflow. The final course brings everything together with CI/CD pipelines, continuous delivery of models, monitoring inference performance and data drift, and implementing retraining and rollback strategies. By the end, you will have the engineering competencies to build, serve, operate, and maintain ML-powered applications across the full production lifecycle.
Projet d'apprentissage appliqué
Throughout the specialization, learners complete hands-on coding labs, API development exercises, MLOps pipeline configurations, and end-to-end deployment projects. You will build and train ML models inside modular software components, serve them as REST and gRPC APIs with versioning and load management, configure experiment tracking with MLflow, and implement data and model version control using DVC and Git. Learners design CI/CD pipelines using Jenkins and GitHub Actions, set up monitoring dashboards with Prometheus for inference performance and data drift detection, and implement testing frameworks covering unit, regression, and integration tests for ML systems. The optional capstone project challenges learners to build a complete production-grade ML application as a recommendation system, fraud detector, or NLP API incorporating model integration, API service layer, version control, CI/CD deployment pipeline, and a monitoring and testing framework ready for portfolio presentation.
