H2O.ai Advances H2O Driverless AI with New Time Series Innovation
New Time Series, Automatic Documentation and Automatic Pipeline Features Enable Better AI Models and Predictive Capabilities for Customers
NEW YORK, June 7, 2018 /PRNewswire/ — H2O World 2018 – H2O.ai, the open source leader in AI, today announced a new release of its award-winning automated machine learning platform H2O Driverless AI, with key enhancements that will enable organizations to expand their current AI strategy and improve accuracy of predictive datasets. The latest innovations in Driverless AI include time series support aimed at improving predictions within transactional datasets and auto documentation, as well as updated AutoViz, Machine Learning Interpretability (MLI) and Automatic Pipelines.
With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, tracking in-store and online sales in retail, and using sensor data to improve supply chain or predictive maintenance in manufacturing.
“Time series is all-pervasive. It is not only one of the fastest growing classes of data but also one particularly well-suited for machine learning. Our latest recipes in Driverless AI automate predictions on time series for demand and inventory forecasting for retail stores, tick level predictions in capital markets and electronic trading, greener manufacturing by optimizing supply chains and securing our world with sensor data from IoT,” said Sri Ambati, CEO and founder at H2O.ai. “Our mission to democratize machine learning – to make it accessible to every enterprise unable to access the highly sought after talent in data science – is one step closer.”
Time Series Helps Forecast Sales, Predict Industrial Machine Failure and More
Driverless AI’s time series feature is ideal for transaction, log and sensor data. Some of the key advantages include the ability to:
- Optimize for almost any prediction time window, whether that be for the next day or the next week
- Incorporate data from numerous predictors, rather than focusing solely on past time series data
- Handle gaps in time series data input without negatively impacting predictive modeling
- Handle missing values, structured character data and high-cardinality categorical variables
- Generate predictions for both numeric and classification problems
In addition to the new time series support, H2O.ai has also added the following to H2O Driverless AI:
- AutoDoc is an early-release feature of Driverless AI which generates a document describing the unique experiment pipeline chosen by Driverless AI and the user settings. The report provides insight into the training data and any detected shifts in distribution, the validation schema selected, model parameter tuning, feature evolution and the final set of features chosen during the experiment.
- Expanded AutoViz capabilities with additional charts and visualizations that enable data scientists to preview and examine a broader range of data sets
- Automatic Pipeline generation that provides the code needed to deploy scored models in applications on any device
- data.table for Python is the data munging engine for Driverless AI and is H2O’s new project based on the popular open source project in R, data.table. It brings the same features to the growing Python community and enables Python users to benefit from data.table’s high performance, superior data processing capabilities, and big data support
H2O Driverless AI empowers data scientists or data analysts to work on projects faster and more efficiently by using automation and state-of-the-art computing power to accomplish tasks that can take humans months in just minutes or hours by delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, time-series and automatic pipeline generation for model scoring.
Also today, H2O.ai announced a strategic global partnership with IBM, focused on combining IBM Power Systems and H2O Driverless AI to address the AI demands of enterprises. Read more about this partnership and joint solution: http://www.h2o.ai/company/news/IBM-and-H2Oai-partnership-aims-to-accelerate-adoption-of-ai-in-the-enterprise
From the Source: Live Stream of H2O World NYC
H2O.ai is fostering a grassroots movement of systems engineers, data scientists, data developers and predictive analysts to move machine learning forward. Today the company will be hosting the first of its 2018 H2O World conferences in New York City at the New York Hilton Midtown. The event was recently moved after demand from attendees far exceeded the capacity of the original venue.
The entire event will be live streamed starting at 9:00am ET. You can follow the day-of livestream here: http://www.h2o.ai
Connect with H2O.ai
- Download Driverless AI for a free 21-day trial: http://www.h2o.ai/try-driverless-ai/
- Visit us to learn more: www.h2o.ai
- Follow us on Twitter: www.twitter.com/H2Oai
- Connect with us on LinkedIn: www.linkedin.com/company/2820918/
H2O.ai is the open source leader in AI. Its mission is to democratize AI for all. H2O.ai is transforming the use of AI with software with its category-creating visionary open source machine learning platform, H2O. More than 14,000 companies use open-source H2O in mission-critical use cases for Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing. H2O.ai recently launched Driverless AI that uses AI to do AI in order to provide an easier, faster and effective means of implementing data science. In February 2018, Gartner named H2O.ai, as a Leader in the 2018 Magic Quadrant for Data Science and Machine Learning Platforms. H2O.ai partners with leading technology companies such as NVIDIA, IBM, AWS, Azure and Google and is proud of its growing customer base which includes Capital One, Progressive Insurance, Comcast, Walgreens and PayPal. For more information and to learn more about how H2O.ai is transforming business processes with intelligence, visit www.h2o.ai.