Simplify your AI lifecycle with AutoAI

Automated Machine Learning (AutoML) tools help in automating the end-to-end process lifecycle that is involved in building and maintaining machine learning models. In this series, 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 Automated Machine Learning(AutoML)
  • Introduction to AutoAI

Build machine learning models with and without AutoML

Learn about:

  • Overview of Automated Machine Learning (AutoML)
  • Two ways to build predictive models, both with and without the help of AutoAI
  • Compare model building with and without AutoAI

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

Generate a Python notebook for pipeline models using AutoAI

Learn about:

  • Run an AutoAI experiment
  • Generate and save a Python notebook
  • Execute the notebook and analyze results
  • Make changes and retrain the model using Watson Machine Learning SDKs
  • Deploy the model using Watson Machine Learning from within the notebook

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