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W7L149G - Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio

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.

Code: w7l149g

Duration: 7.0 hours

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Objectives

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

Content

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

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

Certification

Course Benefits

Career growth
Broad Career opportunities
Worldwide recognition from leaders
Up-to Date technical skills
Popular Certification Badges

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