Deploying Machine Learning Models Coursera

Overview

In this Deploying Machine Learning Models  offered by Coursera in partnership with UC San Diego 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 for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

What you will learn

• 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