Description
Deployment of Machine Learning Models: From Jupyter Notebook to Production
You’ve built a great model—but can it run in the real world? Most data scientists stop at model.fit(). This course teaches you the **full MLOps pipeline**: containerization, REST APIs, CI/CD, monitoring, and cloud deployment on AWS, Heroku, and Docker.
What You’ll Build
- A REST API that serves your model predictions via Flask
- A Docker container for reproducible, portable model deployment
- A CI/CD pipeline using GitHub Actions to automate testing and deployment
- A Heroku-hosted web app with a live prediction endpoint
- A monitoring dashboard to track model drift and performance
Key Concepts Covered
- Model serialization—pickle, joblib, and ONNX
- API design—Flask, FastAPI, request/response validation
- Containerization—Dockerfiles, multi-stage builds, image optimization
- Cloud deployment—Heroku (PaaS), AWS ECS (IaaS)
- Testing & validation—differential testing, shadow mode deployment
Why This Matters
Companies don’t need more notebooks—they need **reliable, scalable, maintainable systems**. This course turns you from a data scientist into a **machine learning engineer**.
Who Should Enroll?
- Data scientists tired of “throwing models over the wall”
- ML engineers preparing for MLOps roles
- Students building deployable capstone projects
- Freelancers delivering end-to-end AI solutions
From Research to Reality
You’ll graduate with a **production-grade deployment workflow**—exactly what top firms look for.
Stop prototyping. Start deploying. Enroll today.
