H2O Hydrogen Torch is democratizing AI by simplifying and streamlining the process of making state-of-the-art deep learning models for all data scientists, from the novice to the expert. Deep learning is able to learn and make intelligent decisions on its own, even with limited and unstructured data available. H2O Hydrogen Torch unlocks value from this unstructured data to help teams understand it at scale. For the enterprise, H2O Hydrogen achieves AI transformation by changing the way they deliver value to both customers and internal teams.
Deep Learning models prove more powerful and flexible than classical machine learning in solving complex problems such as natural language processing (NLP) and computer vision. However, making deep learning models that can address diverse problem types requires extensive data science expertise and time. H2O Hydrogen Torch solves these challenges by providing an intuitive user interface and highly optimized training routines, developed by the world’s top Grandmaster data scientists, for common deep learning problems. With H2O Hydrogen Torch, world-class deep learning models can be rapidly trained and deployed on the H2O AI Cloud to solve the most critical problems that businesses face today.
Optimize e-commerce web search and inventory
Build question-answering systems specific to the business
Improve diagnostic imagery detection for patient care
Locate critical information from medical transcripts
Detect required pieces during assembly process
Categorize incoming emails by urgency
Predict customer satisfaction from audio transcriptions
Simplify texts containing domain-specific terms
How Data Scientists use H2O Hydrogen Torch
Classification and Regression
Classification and Regression
Sequence to Sequence
Live stream object detection
Classification and regression
Democratizes Deep Learning
Train state-of-the-art models with no coding needed
Deploy models easily to external Python environments or directly to H2O MLOps
Quickly experiment with hyperparameters to tune model performance
Ready-to-use library of problem types that continuously expands based on Data Science trends
Train deep learning models using training techniques developed by Kaggle Grandmasters
Achieve results by using the best neural network architectures and transfer learning
Upload your data to H2O Hydrogen Torch using our web interface, allowing you to handle a wide variety of unstructured data, including:
Text (eg, transcriptions, emails) with multi-lingual and domain-specific support
Images (data from photos and image analysis)
Video (data captured into video that can be segmented into frames for analysis)
Audio (speech and any audio recordings)
Set up and manage deep learning experiments to maximize your model development.
Choose model architecture and most relevant tuning parameters
Quick start options provide the most important settings
Expert settings provide a wide range of training approaches
Select the training set and problem type within H2O Hydrogen Torch, and it will automatically learn the data and create models
AI-ML engine that uses multiple computer vision and NLP algorithms for diverse AI tasks (eg, entity recognition, grouping and set identification)
Optimize neural network and perform hyperparameter tuning without the need to change any code
Inspect and analyze your models within the tool.
Track progress during model training, assessing model accuracy and analyzing predictions of the model
Reveal errors in the data that supply ideas on how to improve the model
H2O Hydrogen Torch packages all the models automatically and provides a format which can be published to:
MLOPS, which is part of the H2O AI Hybrid Cloud
Your cloud or on-premises environment of choice
Apply deep learning models made with H2O Hydrogen Torch across a variety of business problems by:
Developing use-case specific apps built with H2O Wave
Importing into existing applications via APIs.
Prediction Insights that give Data Scientists ideas for how to improve models for next iterations.
Streamlined model deployment native to the H2O AI Cloud that can also be imported to other model serving platforms
Comprehensive set of techniques for deep learning model tuning selected from the best practices of Kaggle Grandmasters
Meets novice Data Scientists where they are with a no-code UI and fosters growth opportunities through practical experience and documentation
Multi GPU training support
Supported Deep Learning Problem Types
Text Data and Natural Language Processing (NLP)
Text Classification and Regression
Predict customer satisfaction from transcribed phone calls and categorize incoming emails requesting customer support to send to relevant departments
Extract entities such as disease names from medical text with multi-lingual support
Find relevant information from medical transcripts and build a company-specific question-answering system
Simplify text that contains domain-specific terms and summarize text for better understanding of its content
Detect fake reviews that display similarities and find similar questions in user forums
Image and Video Data
Image Classification and Regression
Predict images for abnormalities in medical X-rays and classify types of landscapes from drone photos.
Detect vehicles from traffic and drone cameras and find objects required for assembly line processes.
Locate objects of interest in medical images, segment roads from video dashcams and remove objects from the background
Detect and delineate distinct objects of interest in biological images, segment individual cars, pedestrians, or other objects from a video captured using a dashcam
Find similar images to those in a training database, such as unauthorized use of a brand logo or duplicate items on a retail website
Featured Success Story
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.”
H2O Hydrogen Torch is available in the H2O AI Cloud. Signup for the 90-day free trial and start exploring.