Introduction to H2O Driverless AI

Introduction to H2O Driverless AI

Course Level: Foundation

H2O Driverless AI is a proprietary tool developed by to perform automatic machine learning without the need for coding. Automatic machine learning is the process of automating the tasks of applying machine learning to real-world problems. Driverless AI provides automatic feature engineering, model validation, model tuning, model selection and deployment and machine learning interpretability all within a user-friendly interface. By the end of this one-day course you will be able to use Driverless AI to create, analyse and deploy machine learning models for your data.

Book: Introduction to H2O Driverless AI

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Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • Introduction to Driverless AI: A brief introduction to H2O Driverless AI including a breakdown of user interface
  • Automated machine learning: A quick overview of automated machine learning
  • Loading and visualising data: How to load in data and visualise it, including the types of data files Driverless AI will accept.
  • Launching an experiment: Discuss through the many options to choose from for modelling data for both regression and classification problems.
  • Interpreting results: Explore feature engineering, summary information, and model interoperability reports provided by Driverless AI along with H2O AutoDoc.
  • Model Deployment: Show how to deploy a Driverless AI model using H2O Cloud and H2O MLOps.

Learning Outcomes

By the end of the course, participants will understand…

  • how to load and model data in Driverless AI
  • how to interpret experiment results
  • how to deploy models created in Driverless AI for production.


Prior Knowledge

A basic understanding of common statistical/machine learning terms and concepts would be an advantage but not essential. The ideas needed for these subjects will be covered within the course but will not be discussed in great depth.

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