Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio V4.8

Introduction

• Discuss the four steps that make up the AI Ladder
• Explain the AI Lifecycle and the different personas that are involved in an AI project
• Explain the components that comprise a Watson Studio project

Rapid prototyping with AutoAI

• Describe the benefits of AutoAI for rapid prototyping
• Discuss the steps needed to run a successful AutoAI experiment
• Interpret and discuss the results from an AutoAI experiment

Creating, testing, and deploying machine learning models

• Discuss various ways of building a machine learning model in Watson Studio by using programming and visual interfaces
• Discuss the contents of the model repository
• Describe the various deployment methods for AI models

Governance, integration, and collaboration

• Describe the capabilities and features to support governance of your data 
• Describe the team collaboration features of Watson Studio

The course is designed for Data Scientists and Business Analysts. It caters to aspiring or practicing Data Scientists, and Business Analysts with prior knowledge of Data Science and Machine Learning, but lacking familiarity with IBM tools and IBM Watson. Rather than teaching data science and machine learning, the course focuses on demonstrating how IBM Watson tools and services can address significant business challenges, targeting professionals at the associate level and beyond.

Before taking this course, you should have:

• Knowledge of Data Science
• Knowledge of Machine Learning
• Experience with the Python programming language

• 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