New no-code tool simplifies training and tuning of image, video and natural language processing models
H2O.ai, the 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.
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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, 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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Said Stelios Anagnostopoulos, CTO at Aura.ceo, “H2O Hydrogen Torch has been a key enabler in helping us operationalize 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.”
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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.
“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 pre-built deep learning models to deliver high accuracy requires a no-code AI Engine to democratize AI for these use cases,” said Sri Ambati, CEO and founder, 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.”
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