Several versions of H2O Wave ML have been released since the last announcement. Wave ML can now run on H2O AI Hybrid Cloud, utilize H2O Driverless AI (DAI) to train the models and push them into H2O MLOps. In addition, new utility functions were introduced to support H2O Enterprise Steam together with MLOps and minor changes to API were made.
Wave ships with a growing library of cards and components for assembling user interfaces. For most apps, the built-in components can be adequate. They're designed to work well with each other, with consistent typography, layout and theming; and the library of components keeps expanding with each new release.
Wave v0.14 is released. This is mainly a security, performance, and bugfix release. The release also includes experimental support for building and controlling Wave dashboards using the R programming language.
Today, we are happy to announce preliminary support for the R language in H2O Wave. We now ship an R package that allows you build and control dashboards in Wave, and has feature-parity with Python Wave Scripts.
Today, we're excited to announce H2O Wave ML, an open-source extension to Wave that makes it easy to use automatic machine learning in your Wave apps.
In this article, we look at what Wave ML can do for you, how to get started, and what predictive applications look like in practice.
Wave v0.12 shipped last week, with support for handling queries and routes using decorators and experimental support for switching themes. Here's a rundown of the major features.
Today, we're excited to announce H2O Wave v0.11.0, with support for responsive layouts, inline form components, new cards for organizing content, and lots more.
Today, we're excited to announce H2O Wave v0.9.0, with a new
wave CLI, live-reload, improved performance, background tasks and ASGI compatibility.