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Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio
Course Description
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.
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
Audience
Data Scientists and Business Analysts
Prerequisites
- 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
The data scientists' and business analysts' prerequisite skills and knowledge include:
Topics
- Introduction
- Rapid prototyping with AutoAI
- Creating, testing, and deploying machine learning models
- Governance, integration, and collaboration
Recognition
When you complete the Instructor-Led version of this course, you will be eligible to earn a Training Badge that can be displayed on your website, business cards, and social media channels to demonstrate your mastery of the skills you learned here.
Learn more about our IBM Watson Badge Program →Related Courses
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This course is eligible for the Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio - Code: W7L149G
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Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
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