A graduate of this program will be able to:

- Test Python code and build a Python package of their own.
- Build predictive models using a variety of unsupervised and supervised machine learning techniques.
- Understand cloud deployment terminology and best practices.
- Use Amazon SageMaker to deploy machine learning models to production environments, such as a
web application or piece of hardware.
- A/B test two different deployed models and evaluate their performance.
- Utilize an API to deploy a model to a website such that it responds to user input, dynamically.
- Update a deployed model, in response to changes in the underlying data source

Project Overview: 

- Build a Python Package: Write a Python package on your own using software engineering best
practices for writing production level code.
- Deploy a Sentiment Analysis Model: Using SageMaker, deploy your own PyTorch sentiment
analysis model, which is trained to recognize the sentiment of movie reviews (positive or negative).
- Plagiarism Detector: Engineer features that can help identify cases of plagiarism in text and deploy
a trained plagiarism detection model using Amazon SageMaker.
- Capstone Project & Proposal: Complete a final project—choosing from a few, provided options or a
project of your own design—that involves data exploration and machine learning.