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Simplify your AI lifecycle with AutoAI

Learn how AutoAI in Watson Studio can automatically prepare data, apply machine learning algorithms, and build model pipelines best suited for your data sets and use cases. To help simplify an AI lifecycle management cycle, AutoAI automates:

  • Data preparation
  • Model development
  • Feature engineering
  • Hyperparameter optimization

Introduction to Watson AutoAI

Learn about:

  • Overview of Watson AutoAI

AutoAI: Humans and machines better together

Learn about:

  • Automating data preparation
  • Automating model development
  • Automating feature engineering
  • Automating hyperparameter optimization

Generate machine learning model pipelines to choose the best model for your problem

Learn about:

  • Benefits of the AutoAI service on a use case
  • How tasks (feature engineering, model selection, and hyperparameter tuning) are performed
  • Details for choosing the best model among the pipelines and how to deploy and use these models

Create a machine learning web app to predict your insurance premium cost

Learn about:

  • Quickly set up the services on IBM Cloud to build the model
  • Ingest the data and initiate the AutoAI process
  • Build different models using AutoAI and evaluate the performance
  • Choose the best model and complete the deployment
  • Generate predictions using the deployed model by making REST calls
  • Compare the process of using AutoAI and building the model manually
  • Visualize the deployed model using a front-end application