Deploying Machine Learning Models

In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products

• Project structure of interactive Python data applications
• Python web server frameworks: (e.g.) Flask, Django, Dash
• Best practices around deploying ML models and monitoring performance
• Deployment scripts, serializing models, APIs