Overview

This course takes the data scientist (or business analyst) on a journey from the creation of several machine learning models to its deployment and testing. Various tools and services as well as programming and graphical user interfaces are used in the process. The course ends with the sharing of assets on GitHub and a brief discussion on governance and stewardship. 

Audience

Data Scientists and Business Analysts

Prerequisites

The data scientists’ and business analysts’ prerequisite skills and knowledge include: 

• Experience working in a browser  
• Some experience writing and running Python programs 
• Some experience with the Jupyter Notebook environment  
• Some experience working with a graphical user interface (GUI) 
• Knowledge of machine learning (regression, decision trees, and random forests) 
• Some experience working with GitHub

Objective

• Define a solution to a business problem using tools and frameworks from IBM Watson Studio 
• Demonstrate how the AI lifecycle can be automated by building a rapid prototype using AutoAI 
• Build, train, and deploy a machine learning model with the tools and services available in Watson Studio 
• Implement GitHub Integration and team collaboration in Watson Studio

Course Outline

• Introduction
• Rapid prototyping with AutoAI
• Creating, testing, and deploying machine learning models
• Governance, integration, and collaboration