
H2O.ai, the AI Cloud leader, today announced H2O Hydrogen Torch, a deep learning training engine that makes it easy for companies of any size in any industry to make state-of-the-art image, video and Natural Language Processing (NLP) models without coding.
Until now, creating deep learning models has required extensive data science knowledge and time. H2O Hydrogen Torch was developed by the world’s best data scientists, Kaggle Grandmasters, and the challenging parts of creating world-class deep learning models are handled automatically by the product. Through a simple, no-code user interface, data scientists and developers can rapidly make models for numerous image, video and NLP processing use cases, including identifying or classifying objects, analysing sentiment or finding relevant information in text.
According to multiple analyst estimates, 80% to 90% of data is unstructured information, yet only a small percentage of organisations are able to derive value from unstructured data. Deep learning models provide the ability to unlock opportunities to transform industries, including healthcare (computer-aided disease detection or diagnosis through the analysis of medical images), insurance (automation of claims and damage analysis from reports and images) and manufacturing (predictive maintenance by analyzing images, video and other sensor data).
Aura.ceo is a unique talent screening platform that offers a data-driven, outside-in perspective on any organisation’s workforce. Using public data from a range of sources, Aura.ceo’s interactive platform enables its customers to evaluate the array of roles, skills and experience inside a company of any size and see how it compares to competitors.
Said Stelios Anagnostopoulos, CTO at Aura.ceo, “H2O Hydrogen Torch has been a key enabler in helping us operationalise Machine Learning for shifting data. We can get from a new dataset to a deployed model and updated tables in our data warehouse in a couple of days instead of weeks.”
Image and Video Processing
For images and videos, H2O Hydrogen Torch can be trained for classification, regression, object detection, semantic segmentation and metric learning. In a medical setting, for example, H2O Hydrogen Torch could analyse medical X-ray images for abnormalities with a “human in the loop” to make the final decision. Other image-based use cases include object detection in a manufacturing facility to determine whether a part is missing or metric learning that alerts an online retailer to duplicate images on a website.
Natural Language Processing
For text-based or NLP use cases, H2O Hydrogen Torch can be trained for text classification and regression, token classification, span prediction, sequence-to-sequence analysis and metric learning. NLP use cases include predicting customer satisfaction from transcribed phone calls to sequence-to-sequence analysis to summarise a large portion of text, such as from medical transcripts, in a few sentences.
These models then can be packaged automatically for easy deployment to external Python environments or in a consumable format directly to H2O MLOps for production.
“Accelerated by COVID-19, video streams, speech, audio podcasts, email and natural language text have become the fastest growing data for our customers in every industry. Transforming and fine-tuning prebuilt deep learning models to deliver high accuracy requires a no-code AI Engine to democratise AI for these use cases,” said Sri Ambati, CEO at and Founder of H2O.ai. “H2O Hydrogen Torch does exactly that by bringing best practices from Grandmasters to tackle problems ranging from improving in-store customer experiences, identifying fashion trends, and discovering vaccines, to saving lives with video enabled drones fighting fires with AI on the edge. With H2O Hydrogen Torch as a core AI Engine of the H2O AI Cloud, our customers can train models in deep learning and better serve their customers and challenge tech giants.”
H2O Hydrogen Torch is part of H2O.ai’s broad and rapidly expanding set of H2O AI Cloud products, including the recently announced H2O AI Feature Store and H2O Document AI. Customers can try and experiment with H2O Hydrogen Torch for free.


Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)