Machine Learning in Production

Description

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.

The path from notebook to product

• A professional workflow: framing a business problem, project structure and Git for ML, reproducibility and experiment tracking, clean reusable code and testing it with pytest.
• Deploying models: a prediction API with FastAPI, demo apps with Streamlit, Docker basics and deploying to the cloud.
• Monitoring and responsible ML: data drift and model decay, logging and monitoring predictions, explaining models with SHAP and the basics of bias and fairness.
• Building with LLMs: using an LLM API and prompting basics, embeddings and semantic search, a simple RAG app and evaluating LLM outputs.

How you follow along

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 and files

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.

What is included

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.

Who it is for

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.