MLOps: Build & Deploy ML Systems
About this course
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.
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What you'll learn
- Track experiments and reproduce machine learning results reliably
- Automate training, testing, and delivery using CI/CD pipelines
- Scale training and orchestration in cloud-native environments
- Monitor production models for drift and apply governance strategies
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