MLOps: Deploying Machine Learning Models
Packaging, serving, monitoring and retraining models in production.
Taught by Suman Ghimire · DevOps Engineer
What you'll learn
- Log, version and promote models through an MLflow model registry with reproducible metadata
- Serve a scikit-learn model as a REST endpoint with FastAPI and package it in a Docker image
- Choose between batch and online inference and implement each with the right tooling
- Detect data drift and performance decay with monitoring reports and alerts
- Build a CI/CD pipeline that tests, builds and deploys a model on every merge
- Design safe rollouts with shadow, canary and rollback strategies
About this course
This MLOps course is for data scientists and engineers who can train a model in a notebook but have never shipped one. You will learn how to package a trained model with a model registry, serve it as a REST API with FastAPI and Docker, choose between batch and online inference, monitor data drift and model performance once real traffic arrives, and automate testing, deployment and retraining with CI/CD for machine learning. Every lesson is built around a working example: a churn model that goes from a pickle file to a monitored, versioned service. You should be comfortable with Python and scikit-learn and have used the command line; prior Docker or cloud experience helps but is not required. By the end you will have a repeatable deployment pipeline you can apply to any model, and the vocabulary to work confidently alongside platform and DevOps teams.
Course content
7 lessons- 1Why Models Fail After the NotebookWhat MLOps is, why many models never reach production, and the lifecycle you will build.Preview
- 2Packaging and Versioning ModelsExperiment tracking, model registries and reproducible artefacts with MLflow.
- 3Serving Models With FastAPI and DockerWrap a model in a validated REST endpoint and package it into a reproducible container.
- 4Batch Versus Online InferenceWhen to precompute predictions on a schedule, when to serve live, and how feature stores keep them consistent.
- 5Monitoring Drift and Model HealthDetect changing inputs, decaying accuracy and broken pipelines before users do.
- 6CI/CD and Automated RetrainingTest ML code, build images and deploy on merge, then retrain on a trigger without a human in the loop.
- 7Safe Rollouts and Production ReadinessShadow, canary and rollback strategies, plus the checklist to sign off a model for production.