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There is 1 module in this course
This intermediate-level course is designed for machine learning engineers and developers who want to move beyond experiments and ship reliable ML systems. Learners will learn how to apply core MLOps practices such as version control, pull requests, and CI/CD pipelines to keep an ML codebase healthy and production-ready. Learners will also design modular software components and build a FastAPI microservice that serves a transformer model through a clean, well-defined API.
Through short videos, guided coaching conversations, hands-on learning activities, and an ungraded lab, Learners will practice real workflows used by ML teams in industry. By the end of the course, Learners will be able to confidently collaborate on ML codebases, pass automated quality checks, and deploy machine learning models behind scalable APIs.
This intermediate-level course is designed for machine learning engineers and developers who want to move beyond experiments and ship reliable ML systems. Learners will learn how to apply core MLOps practices such as version control, pull requests, and CI/CD pipelines to keep an ML codebase healthy and production-ready. Learners will also design modular software components and build a FastAPI microservice that serves a transformer model through a clean, well-defined API. Through short videos, guided coaching conversations, hands-on learning activities, and an ungraded lab, Learners will practice real workflows used by ML teams in industry. By the end of the course, Learners will be able to confidently collaborate on ML codebases, pass automated quality checks, and deploy machine learning models behind scalable APIs.
What's included
6 videos2 readings3 assignments1 ungraded lab
Show info about module content
6 videos•Total 26 minutes
Course Introduction & Welcome •4 minutes
From Notebook to Production ML•4 minutes
CI/CD Pipelines and Automated Testing for ML•5 minutes
From Model Artifact to API Service•4 minutes
Designing Clean Prediction APIs with FastAPI•5 minutes
Congratulations and Continuous Learning Journey•3 minutes
2 readings•Total 18 minutes
GitFlow and Pull Requests for ML Teams•10 minutes
Using Protobuf for ML Inference Requests•8 minutes
3 assignments•Total 50 minutes
Hands-On Activity: Reviewing a Pull Request with CI Checks•15 minutes
Hands-On Activity: Sketching a /predict API Contract•15 minutes
Graded Assessment: Production-Ready ML APIs and MLOps •20 minutes
1 ungraded lab•Total 60 minutes
Build and Validate a Production-Style ML API•60 minutes
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Is financial aid available?
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