Tepper · MS in Business Analytics
46-887 Machine Learning for Business Applications
Taking a model out of the notebook and into a working business system on AWS.
Applied machine learning for MS in Business Analytics students, built around a cloud pipeline on AWS.
Topics
- From machine learning models to AI systems
- Integrating models into business services, with a cloud setup lab
- Evaluating, testing, and monitoring machine learning systems
- Natural language processing and large language models
- Imbalanced classification and anomaly detection
- Time series forecasting, with a cloud-connected Tableau dashboard
- Reinforcement learning
How the course works
The course takes models out of the notebook and into production. Students put data in S3, score it with Lambda and SageMaker, write predictions to a database they provision themselves on RDS, and read them live in Tableau or Streamlit.
Accuracy is the first number students learn to distrust. The fraud lab opens with a one-line model that never flags fraud and is still 99.8% accurate, then works through class weights, SMOTE, and isolation forests on a precision-recall leaderboard. In ML Project Triage, teams sit on an insurer's strategy board with money for two of six proposals, among them a drone moonshot with no data and a pricing engine that would be illegal in most states.
The team project is a working proof of concept: a model, a cloud database of its predictions, and a dashboard, shown in a live demo.
How the course developed
I redesigned this MS in Business Analytics course for Spring 2026 around one question: how a trained model becomes part of a working business system. Mondays are lecture and Wednesdays are lab. Students build pipelines on AWS, put the results in Tableau or Streamlit dashboards, and finish with a team project demo.
Offerings
- Spring 2026
Enrolled students get the lab notebooks on Canvas.