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URL: https://www.coursera.org/learn/develop-production-ready-ml-apis-with-mlops

⇱ Develop Production-Ready ML APIs with MLOps | Coursera

Develop Production-Ready ML APIs with MLOps

Develop Production-Ready ML APIs with MLOps

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

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Shareable certificate

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Recently updated!

March 2026

Assessments

3 assignments¹

AI Graded see disclaimer
Taught in English

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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

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
  • Graded Assessment: Production-Ready ML APIs and MLOps •20 minutes
  • Hands-On Activity: Reviewing a Pull Request with CI Checks•15 minutes
  • Hands-On Activity: Sketching a /predict API Contract•15 minutes
1 ungraded lab•Total 60 minutes
  • Build and Validate a Production-Style ML API•60 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.