H2O.ai Blog
By: Venkatesh Yadav, VP of Engineering
We are excited to announce the new release of H2O Driverless AI with lots of improved features.
Below are some of the exciting new features we have added:
Version 1.6.1 LTS (April 18, 2019) – Available here
- Several improvements for MLI (partial dependence plots, Shapley values)
- Improved documentation for model deployment, time-series scoring, AutoViz and FAQs
Version 1.6.0 (April 5, 2019)
- Improved import of string columns larger than 2GB
- Stabilized AutoViz on Windows
- Improved quantile binning in MLI
- Plot global absolute mean Shapley values instead of global mean Shapley values in MLI
- Improvements to PDP/ICE plots in MLI
- Validated Terraform version in AWS Lambda deployment
- Added support for NULL variable importance in AutoDoc
- Made Variable Importance table size configurable in AutoDoc
- Improved support for various combinations of data import options being enabled/disabled
- CUDA is now part of distribution for easier installation
- Security updates:
- Enforced SSL settings to be honored for all h2oai_client calls
- Added config option to prevent using LocalStorage in the browser to cache information
- Upgraded Tornado server version to 5.1.1
- Improved session expiration and autologout functionality
- Disabled access to Driverless AI data folder in file browser
- Provided an option to filter content that is shown in the file browser
- Use login name for HDFS impersonation instead of predefined name
- Disabled autocomplete in login form
- Cleaned up various bugs
Please see links below for additional information on H2O Driverless AI:
Release Notes:
http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/release_notes.html
Webinars:
Driverless AI Documentation:
http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/index.html
If you have any questions, please reach out to support@h2o.ai
Thanks,
Venkatesh Yadav
About the Author
Venkatesh Yadav, VP of Engineering
Software Engineering Leader at heart with a focus on building great teams that delivers amazing products and customer happiness. Venkatesh serves H2O as VP of Engineering. He joined the company from Adobe Systems, where he held a number of positions in the Software Engineering and Leadership space including his latest role as Sr. Manager, Software Engineering and Product Management with primary focus on Master Data Management and Data Science. Venkatesh played an instrumental Engineering and Product Management leadership role as an “Entrepreneur in Residence” in the various key strategic programs and initiatives like Adobe@Adobe, Adobe.io and Adobe.Data. Experience of managing and working with teams across the globe in US, Canada, Switzerland, Romania, India with a focus on value creation. Prior to Adobe Systems Venkatesh has served technology companies in various engineering roles in companies like Philips, HP and IBM. Venkatesh holds a Bachelor of Commerce degree from Mumbai University India and has successfully completed Product Management program from UC Berkeley and General Business Administration and Management program from McGill University. Connect with Venkatesh (@venkateshai)
Enhancing H2O Model Validation App with h2oGPT Integration
As machine learning practitioners, we’re always on the lookout for innovative ways to streamline and
May 17, 2023 - by Parul Pandey
Building a Manufacturing Product Defect Classification Model and Application using H2O Hydrogen Torch, H2O MLOps, and H2O Wave
Primary Authors: Nishaanthini Gnanavel and Genevieve Richards Effective product quality control is of utmost importance in
May 15, 2023 - by Shivam Bansal
Insights from AI for Good Hackathon: Using Machine Learning to Tackle Pollution
At H2O.ai, we believe technology can be a force for good, and we're committed to
May 10, 2023 - by Parul Pandey and Shivam Bansal
Democratization of LLMs
Every organization needs to own its GPT as simply as we need to own our
May 8, 2023 - by Sri Ambati
