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IBM watsonx.ai: Rapid Machine Learning Model Development and Deployment with AutoAI
Course Description
Overview
IBM watsonx.ai: Rapid Machine Learning Model Development and Deployment with AutoAI aims to familiarize data science and analytics professionals with the fundamentals of the IBM watsonx.ai AutoAI tool. This course walks users through creating IBM Cloud projects, building, and evaluating AutoAI experiments for various supervised machine learning and time series use cases, and finally, learners leverage Chat in the Prompt Lab for further analysis of the use case.
The course guides participants through AutoAI features, from model development to deployment, using a no-code approach for:
- Classification models
- Text classification models
- Regression models
- Time series models
- Hyperparameter tuning
- Model explainability
- Data imputation
- Model evaluation
- Model testing
- Deployment
Objectives
By the end of the course, learners will be able to:
- Identify potential machine learning use cases applicable to AutoAI.
- Differentiate problem types relevant to AutoAI experiments (Classification, Regression, Time Series).
- Configure settings for various AutoAI experiments.
- Evaluate pipelines and models produced by AutoAI experiments.
- Recognize deployment strategies for AutoAI models.
Audience
This course is intended for Data Scientists, AI Specialists, watsonx Specialists, Solution Architects, or anyone interested in AutoAI.
Topics
The following topics will be covered throughout the course:
- Introduction to AutoAI
- Classification model development and deployment
- Regression model development
- Text classification model development
- Time series model development
- Model explainability
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