Most machine learning models never reach production, and those that do often fail silently once they are live. This specialization gives you the operational skills to move models from experimentation to dependable, scalable systems that keep performing after launch.
You will begin with the foundations of MLOps: tracking experiments, versioning data, and reproducing results, then automating training and delivery with CI/CD and a model registry. From there, you scale up by building feature pipelines and feature stores, running distributed training and orchestration on Kubernetes and Kubeflow, and serving models with modern frameworks. The final course takes an enterprise view: managed platforms, governance, explainability, bias and fairness checks, drift monitoring, and continuous learning.
By the end of this specialization, you will be able to:
Track experiments, version data, and reproduce machine learning results reliably
Automate training, testing, and delivery using CI/CD pipelines and a model registry
Scale training, orchestration, and model serving in cloud-native environments
Monitor production models for drift and apply governance and retraining strategies
This specialization is designed for ML engineers, AI engineers, data scientists, and software developers with a basic grasp of Python and machine learning who want to operate models in production.
Join us and build the skills to deliver reproducible, scalable, production-ready ML systems.
Projet d'apprentissage appliqué
Across the specialization, you work through hands-on demonstrations and exercises that mirror how real ML teams operate. You set up experiment tracking and data versioning, build automated CI/CD pipelines with a model registry, and package and deploy a model as a scalable prediction service using FastAPI and Docker. As you progress, you build feature pipelines and feature stores, run distributed training and orchestration on Kubernetes and Kubeflow, and serve models with frameworks such as BentoML and KServe. In the final stage, you implement governance, bias and fairness checks, drift monitoring with Evidently AI and Grafana, and retraining workflows for enterprise-scale systems.
