MLOps Foundations: CI/CD, Monitoring, and Deployment
Learn MLOps from scratch — build CI/CD pipelines for ML models, set up monitoring, and deploy to production. Beginner-friendly, free 8-week course.
Course Content
MLOps Foundations
Most machine learning courses end at training a model. This one starts there — and takes you all the way to monitored, production-deployed systems that teams can actually rely on.
The MLOps Gap
Over 85% of ML models never make it to production. The reason isn’t model quality — it’s the infrastructure, automation, and operational practices that sit between a trained model and a running service. MLOps closes that gap.
This course teaches you the practices and tools that professional ML teams use to ship reliably: from experiment tracking and CI/CD to model serving, monitoring, and rollback.
What You’ll Build
- Automated training pipeline: GitHub Actions workflow that trains, tests, and registers a model on every push
- Model serving API: FastAPI + Docker service that returns predictions in under 10ms
- Monitoring dashboard: Live view of model accuracy and data drift for a production deployment
Tools Covered
MLflow · DVC · GitHub Actions · Docker · FastAPI · Evidently AI (monitoring) · MLflow Model Registry · Kubernetes (intro)
Who This Is For
This course is designed for ML practitioners who know how to train models but struggle to get them into production — and for software engineers making the transition into ML engineering roles.
📋 Prerequisites
- Basic Python programming
- Familiarity with training a machine learning model (any framework)
- Basic command-line usage
🎯 What You'll Learn
- Set up reproducible ML projects with version control for code, data, and models
- Build CI/CD pipelines that automatically test and deploy ML models
- Serve models as production REST APIs with FastAPI and Docker
- Monitor models in production for data drift and performance degradation
- Manage model versions using a model registry