Students from the SAIT's Applied Machine Learning Bootcamp and our Applied Product Management Bootcamp participate in a 78 hour interdisciplinary machine learning capstone project. This project culminates in the development of a machine learning model that predicts, detects, or forecasts an entity. The data for the use case could be images (computer vision), text (natural language processing), time series (multi-variate or univariate), or tablular data. The data format would be a folder of images or comma-separated values (CSVs) for text, time series, or tablular data. The client will need to:

1) Provide a clearly defined machine learning problem.

2) Explain how the client intends to use the solution.

3) Explain why this problem needs to be solved.

4) Provide a subject matter expert that can be a touch point for the student and answer questions related to the data and use case.

Students will produce a proof of concept, predictive machine learning model (i.e. a minimally viable product) that solves a client problem.