Customize and deploy open source AI models, create your own digital assistants and business GPTs.
Open weight small vision-language models for OCR and Document AI.
Industry and Use Case AI Apps
From Credit Scoring and Customer Churn to Anti-Money Laundering
From Clinical Workflow to Predicting ICU Transfers
From Claims Management to Fraud Mitigation
From Predictive Maintenance to Transportation Optimization
From Content Personalization to Lead Scoring
From Assortment Optimization to Pricing Optimization
From Predictive Customer Support to Predictive Fleet Maintenance
Learn how USCF Health is applying H2O Document AI to automate workflows in healthcare
Learn how AES is transforming its energy business with AI and H2O.ai
Learn now IFFCO-Tokio uses the H2O AI Cloud to save over $1M annually by transforming their fraud prediction processes
Learn how Epsilon is increasing its customers' marketing ROI with H2O.ai
Gain expertise through engaging courses and earn certifications to thrive on your AI journey.
Get help and technology from the experts in H2O and access to Enterprise Team
Read the H2O.ai wiki for up-to-date resources about artificial intelligence and machine learning.
Learn the best practices for building responsible AI models and applications
By H2O.ai Team | minute read | December 02, 2017
Dear H2O Community,
#H2OWorld is on Monday and we can’t wait to see you there! We’ll also be live streaming the event starting at 9:25am PST. Explore the agenda here .
Today we’re excited to share that new versions of H2O-3 and Sparkling Water are available.
We invite you to download them here:
http://www.h2o.ai/download/
H2O-3.16
– MOJOs are now supported for Stacked Ensembles.
– Easily specify the meta-learner algorithm type that Stacked Ensemble should use. This can be AUTO, GLM, GBM, DRF or Deep Learning .
– GBM, DRF now support custom evaluation metrics.
– The AutoML leaderboard now uses cross-validation metrics (new default).
– Multiclass stacking is now supported in AutoML . Removed the check that caused AutoML to skip stacking for multiclass.
– The Aggregator Function is now exposed in the Python/R client.
– Support for Python 3.6.
Detailed changes and bug fixes can be found here:
https://github.com/h2oai/h2o-3/blob/master/Changes.md
Sparkling Water 2.0, 2.1, 2.2
– Support for H2O Models into Spark python pipelines.
– Improved handling of sparse vectors in internal cluster.
– Improved stability of external cluster deployment mode.
– Includes latest H2O-3.16.0.2.
Detailed changes and bug fixes can be explored here:
2.2 – https://github.com/h2oai/sparkling-water/blob/rel-2.2/doc/CHANGELOG.rst
2.1 – https://github.com/h2oai/sparkling-water/blob/rel-2.1/doc/CHANGELOG.rst
2.0 – https://github.com/h2oai/sparkling-water/blob/rel-2.0/doc/CHANGELOG.rst
Hope to see you on Monday!
The H2O.ai Team
At H2O.ai, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI.
Today we have evolved into a global company built by people from a variety of different backgrounds and skill sets, all driven to be part of something greater than ourselves. Our partnerships now extend beyond the open-source community to include business customers, academia, and non-profit organizations.
Make data and AI deliver meaningful and significant value to your organization with our platform.