title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.
Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio V4.8
Course Description
Overview
The focus of this course is on the tools and services available in IBM Watson Studio that can be used to build, test, and deploy machine learning models on Cloud Pak for Data V4.8. It 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
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.
Prerequisites
- Knowledge of Data Science
- Knowledge of Machine Learning
- Experience with the Python programming language
Before taking this course, you should have:
Topics
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
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 Badge Program →
This course is eligible for the Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio V4.8 - Code: W7L549G
Need Help?
Call us at 877-206-0106 or e-mail us at info@learnquest.com
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- 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.
- Learn more about e-Learning
Course Added To Shopping Cart
bla
bla
bla
bla
bla
bla
Self-Paced Training Terms & Conditions
Exam Terms & Conditions
Sorry, there are no classes that meet your criteria.
Please contact us to schedule a class.
STOP! Before You Leave
Save 10% on this course!
Take advantage of our online-only offer & save 10% on any course !
Promo Code IBM10 will be applied to your registration
Purchase Information
title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.