H2O.ai Democratizes Deep Learning with H2O Hydrogen Torch

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H2O.ai, an AI Cloud leader, 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 to code and tune accurate models. 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 in images and video and analyzing 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 organizations 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).

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“H2O Hydrogen Torch has been a key enabler in helping us operationalize machine learning for shifting data,” said Stelios Anagnostopoulos, CTO at Aura.ceo. “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 analyze 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 summarize 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.

“Our customers from every industry are generating exabytes of unstructured data every year. Unstructured data is the fastest growing source of data in the enterprise, ranging from customer conversations to retail brick and mortar video streams,” said Sri Ambati, CEO and founder, H2O.ai. “H2O Hydrogen Torch is the latest step in H2O.ai’s mission to democratize AI. With H2O Hydrogen Torch and H2O AI Cloud, all organizations now have the freedom to innovate with deep learning to better serve their customers.” 

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. 

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