Develop Production-Ready ML APIs with MLOps

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

• Collaborate confidently on ML codebases
• Apply MLOps practices including version control and pull requests
• Implement CI/CD pipelines to maintain code health
• Design modular software components
• Build FastAPI microservices to serve ML models

• Basic familiarity with ML concepts and terminology
• Readiness for applied exercises and case studies