H2O.ai Accelerates AI Deployments for Customers with Automatic Pipelines Generated with Driverless AI

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H2O.ai, an open leader in AI, today announced a new release of its award-winning automated machine learning platform Driverless AI, with key enhancements that continue to democratize AI for the enterprise. The latest innovations in Driverless AI include accelerated automatic pipelines of feature engineering and machine learning to generate highly optimized, low-latency production-ready code for deployment on the edge. This is ideal for enterprises that need to deploy low-latency scoring engines that can deliver sub-millisecond inferencing for real-time applications to a range of devices.

H2O.ai is doing a great job in enhancing the product at such a rapid rate. Each release provides significant increases in usability and value. Driverless AI gives startups like ours an effective alternative to large data science teams and their outsized cost. It can dramatically reduce the time needed to deliver first-rate ML models for a wide range of markets,” said Marc Stein, CEO at Underwrite.ai.

Driverless AI combines open source frameworks with the power of GPUs to create an extensible platform to build data science recipes that solve an entire class of problems for the industry. In addition, protection from over-fitting and signal leakage enables users to build models and deploy pipelines safely. Automatic visualization (AutoViz) and model interpretability (MLI) are included in Driverless AI to  provide transparency and  explainability.

Automatic Pipelines with Driverless AI brings advanced AI techniques into the hands of every engineer and data scientist in the enterprise. Essentially, it gives every enterprise the power of an expert data scientist in a box,” said Sri Ambati, CEO at H2O.ai. “Our mission to democratize AI starts by making training and inference faster, cheaper, easier and safer. With AutoViz and machine learning interpretability, Driverless AI is building trust in AI for all.”

The latest innovations in Driverless AI include:

  • New automated pipelines that are well suited for real-time or online applications
  • New charts and visualizations for model diagnostics
  • Support for Amazon S3, Google Cloud Storage and Google BigQuery
  • Support for HDFS for ingesting data from Apache Hadoop

Driverless AI powers our data science team to operate efficiently and experiment at scale,” said Bharath Sudarshan, Director of Data Science and Innovation at ArmadaHealth, a fast-growing healthcare company focusing on doctor recommendations and patient journeys. “With this latest innovation, we have the opportunity to impact care at large.”

Driverless AI automates some of the most difficult data science and machine learning workflows such as feature engineering, model validation, model tuning, model selection and model deployment. In addition, H2O.ai customers are generating over one million models and machine learning pipelines each month with Driverless AI.


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