Take machine learning into the real world: structure and test ML code, serve models with FastAPI and Docker, deploy to the cloud, monitor and explain them, build LLM apps with RAG, and ship a capstone.
Level
04
Modules
05
Lessons
22
Introduction
Framing a Business Problem as an ML Problem
Downloadable Assets and Resources
Project Structure and Git for ML
Reproducibility and Experiment Tracking
Writing Clean, Reusable ML Code
Testing ML Code with pytest
Assessment
A model in a notebook helps nobody. This course is about everything that happens after the notebook: turning a business problem into an ML problem, structuring and testing the code, serving predictions through an API, packaging and deploying it, watching it decay in production and explaining its decisions. It ends with a capstone in which you build and ship a loan risk application yourself.
You work in your own editor and terminal as well as in notebooks, because production code is not a notebook. Each lesson explains the idea with the reasoning and limits, shows the code, and gives exercises with hints. Some steps, such as building a Docker image, deploying to a cloud provider and calling paid LLM APIs, are shown with the exact commands so that you can run them on your own account; the lessons say clearly which parts were run for the printed output and which are shown for you to try.
The capstone, Deployed Loan Risk App, comes with a template repository and a worked solution repository (zip files), a loan applications dataset and a second version with drifted data for the monitoring lessons, a small collection of company FAQ documents and a set of test questions for the RAG part. The four capstone lessons take you through the brief, data, model and experiments, then tests, explanations, API and Docker, and finally deploying, monitoring and presenting.
Lessons that follow a model from framing the problem to deployment, monitoring and LLM applications, a capstone template and a worked solution, the loan data with a drifted version, FAQ documents and test questions for the RAG part, and a quiz for every module.
Plenty of people can train a model. Far fewer can ship one that keeps working. This course closes that gap and takes you from notebook to a deployed, monitored application, with a capstone you can point to. If you want your machine learning to leave the notebook and reach real users, enroll now and learn the skills that turn models into products.
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