Driverless AI – Introduction, Hands-On Lab and Updates

Driverless AI – Introduction, Hands-On Lab and Updates

#H2OWorld was an incredible experience. Thank you to everyone who joined us!

There were so many fascinating conversations and interesting presentations. I’d love to invite you to enjoy the presentations by visiting our YouTube channel.

Over the next few weeks, we’ll be highlighting many of the talks. Today I’m excited to share two presentations focused on Driverless AI – “Introduction and a Look Under the Hood + Hands-On Lab” and “Hands-On Focused on Machine Learning Interpretability”.

Slides available here.

Slides available here.

The response to Driverless AI has been amazing. We’re constantly receiving helpful feedback and making updates.

A few recent updates include:

Version 1.0.11 (December 12 2017)
– Faster multi-GPU training, especially for small data
– Increase default amount of exploration of genetic algorithm for systems with fewer than 4 GPUs
– Improved accuracy of generalization performance estimate for models on small data (< 100k rows)
– Faster abort of experiment
– Improved final ensemble meta-learner
– More robust date parsing

Version 1.0.10 (December 4 2017)
– Tooltips and link to documentation in parameter settings screen
– Faster training for multi-class problems with > 5 classes
– Experiment summary displayed in GUI after experiment finishes
– Python Client Library downloadable from the GUI
– Speedup for Maxwell-based GPUs
– Support for multinomial AUC and Gini scorers
– Add MCC and F1 scorers for binomial and multinomial problems
– Faster abort of experiment

Version 1.0.9 (November 29 2017)
– Support for time column for causal train/validation splits in time-series datasets
– Automatic detection of the time column from temporal correlations in data
– MLI improvements, dedicated page, selection of datasets and models
– Improved final ensemble meta-learner
– Test set score now displayed in experiment listing
– Original response is preserved in exported datasets
– Various bug fixes

Additional release notes can be viewed here:
http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/release_notes.html

If you’d like to learn more about Driverless AI, feel free to explore these helpful links:
– Driverless AI User Guide: http://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/index.html
– Driverless AI Webinars: https://webinar.com/channel/4a90aa11b48f4a5d8823ec924e7bd8cf
– Latest Driverless AI Docker Download: https://www.h2o.ai/driverless-ai-download/
– Latest Driverless AI AWS AMI: Search for AMI-id : ami-d8c3b4a2
– Stack Overflow: https://stackoverflow.com/questions/tagged/driverless-ai

Want to try Driverless AI? Send us a note.

New versions of H2O-3 and Sparkling Water available

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:
https://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

H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise

Driverless AI


Series C round led by Wells Fargo and NVIDIA

MOUNTAIN VIEW, CA – November 30, 2017 – H2O.ai, the leading company bringing AI to enterprises, today announced it has completed a $40 million Series C round of funding led by Wells Fargo and NVIDIA with participation from New York Life, Crane Venture Partners, Nexus Venture Partners and Transamerica Ventures, the corporate venture capital fund of Transamerica and Aegon Group. The Series C round brings H2O.ai’s total amount of funding raised to $75 million. The new investment will be used to further democratize advanced machine learning and for global expansion and innovation of Driverless AI, an automated machine learning and pipelining platform that uses “AI to do AI.”

H2O.ai continued its juggernaut growth in 2017 as evidenced by new platforms and partnerships. The company launched Driverless AI, a product that automates AI for non-technical users and introduces visualization and interpretability features that explain the data modeling results in plain English, thus fostering further adoption and trust in artificial intelligence.

H2O.ai has partnered with NVIDIA to democratize machine learning on the NVIDIA GPU compute platform. It has also partnered with IBM, Amazon AWS and Microsoft Azure to bring its best-in-class machine learning platform to other infrastructures and the public cloud.

H2O.ai co-founded the GPU Open Analytics Initiative (GOAI) to create an ecosystem for data developers and researchers to advance data science using GPUs, and has launched H2O4GPU, a collection of the fastest GPU algorithms on the market capable of processing massive amounts of unstructured data up to 40x faster than on traditional CPUs.

“AI is eating both hardware and software,” said Sri Ambati, co-founder and CEO at H2O.ai. “Billions of devices are generating unprecedented amounts of data, which truly calls for distributed machine learning that is ubiquitous and fast. Our focus on automating machine learning makes it easily accessible to large enterprises. Our maker culture fosters deep trust and teamwork with our customers, and our partnerships with vendors across industry verticals bring significant value and growth to our community. It is quite supportive and encouraging to see our partners lead a significant funding round to help H2O.ai deliver on its mission.”

“AI is an incredible force that’s sweeping across the technology landscape,” said Jeff Herbst, vice president of business development at NVIDIA. “H2O.ai is exceptionally well positioned in this field as it pursues its mission to become the world’s leading data science platform for the financial services industry and beyond. Its use of GPU-accelerated AI provides powerful tools for customers, and we look forward to continuing our collaboration with them.”

“It is exhilarating to have backed the H2O.ai journey from day zero: the journey from a PowerPoint to becoming the enterprise AI platform essential for thousands of corporations across the planet,” said Jishnu Bhattarcharjee, managing director at Nexus Venture Partners. “AI has arrived, transforming industries as we know them. Exciting scale ahead for H2O, so fasten your seat belts!”

As the leading open-source platform for machine learning, H2O.ai is leveling the playing field in a space where much of the AI innovation and talent is locked up inside major tech titans and thus inaccessible to other enterprises. This is precisely why over 100,000 data scientists, 12,400 organizations and nearly half of the Fortune 500 have embraced H2O.ai’s suite of products that pack the productivity of an elite data science team into a single solution.

“We are delighted to lead H2O.ai’s funding round. We have been following the company’s progress and have been impressed by its high-caliber management team and success in establishing an open-source machine learning platform with wide adoption across many industries. We are excited to support the next phase of their development,” said Basil Darwish, director of strategic investments at Wells Fargo Securities.

Beyond its open source community, H2O.ai is transforming several industry verticals and building strong customer partnerships. Over the past 18 months, the company has worked with PwC to build PwC’s “GL.ai,” a revolutionary bot that uses AI and machine learning to ‘x-ray’ a business and detect anomalies in the general ledger. The product was named the ‘Audit Innovation of the Year‘ by the International Accounting Bulletin in October 2017.

H2O’s signature community conference, H2O World will take place on December 4-5, 2017 at the Computer History Museum in Mountain View, Calif.

About H2O.ai

H2O.ai’s mission is to democratize machine learning through its leading open source software platform. Its flagship product, H2O.ai empowers enterprise clients to quickly deploy machine learning and predictive analytics to accelerate business transformation for critical applications such as predictive maintenance and operational intelligence. H2O.ai recently launched Driverless AI, the first solution that allows any business — even ones without a team of talented data scientists — to implement AI to solve complex business problems. The product was reviewed and selected as Editor’s Choice in InfoWorld. Customers include Capital One, Progressive Insurance, Comcast, Walgreens and Kaiser Permanente. For more information and to learn more about how H2O.ai is transforming businesses, visit www.h2o.ai.

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