ML Production Systems
About this course
This Specialization equips you with the end-to-end skills needed to move machine learning models from development into robust production systems. You'll learn to containerize and deploy ML models using Docker and Kubernetes, build RESTful inference services with CI/CD automation, optimize hyperparameters systematically, and construct automated scikit-learn pipelines. The program also covers test-driven development practices for reliable ML code, advanced Kubernetes resource optimization for scalable infrastructure, and Git-based workflows for managing production codebases. Through hands-on projects and practical exercises, you'll gain the MLOps expertise that modern AI teams demand—bridging the gap between data science experimentation and production engineering to deliver ML systems that are reliable, scalable, and maintainable.
63/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Containerize and deploy ML models using Docker and Kubernetes
- Build RESTful inference services with CI/CD automation
- Optimize hyperparameters systematically
- Construct automated scikit-learn pipelines
- Apply test-driven development practices for ML code
- Optimize Kubernetes resources for scalable ML infrastructure
- Manage production ML codebases using Git-based workflows
Course objectives
- Bridge the gap between data science experimentation and production engineering
- Develop end-to-end MLOps skills for deploying robust production ML systems
- Build reliable, scalable, and maintainable ML infrastructure
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