H2O Hydrogen Torch
H2O Hydrogen Torch enables all data scientists to build no-code deep learning models for image, video and text data.
H2O Hydrogen Torch enables all data scientists, from novice to expert, to train no-code deep learning models. State-of-the-art neural network architectures and transfer learning allow data scientists to build accurate deep learning models even with limited data available. Newly designed and researched methods for model training can significantly improve the quality of the models, when applied properly. However, training neural networks can be complex, requiring careful tuning of the training process to get the best outcome. With H2O Hydrogen Torch, novice data scientists can gain experience in deep learning and master best practices, while solving their business problems. Experienced data scientists can dive deeper and focus their time on tuning models, rather than on writing and debugging code.
Why H2O Hydrogen Torch?
H2O Hydrogen Torch democratizes deep learning with:
Web UI to manage data and experiments: H2O.ai provides data scientists with no-code deep learning model training. Choice of the model architecture and all the relevant tuning parameters are available at the complexity level of your choice. Novice users can have a quick start by choosing only the most important settings, while experts can enjoy the ability to enable and tune a wide range of training approaches.
Inspection and analytics: Inspect and analyze your models within the tool. While the model is training, the users can track the progress, assess the accuracy and analyze the predictions of the model. The insights can help learn more about the problem, reveal errors in the data and give ideas how to improve the model.
Model training best practices: Designed by the world's best Kaggle grandmasters, H2O Hydrogen Torch uses methods that were proved to be the most successful across multiple deep learning problems and won Kaggle competitions.
Optimal model search and tuning: Search and tune optimal model and training parameters to build the most accurate model. As training a model is an iterative process, H2O Hydrogen Torch helps speed up the search of the optimal parameters, resulting in the most accurate model for your problem.
Streamlined model deployment: Flexible deployment options either inside the H2O AI Cloud or to an external environment brings your deep learning models to production is easy and integrated with our platform. We package all the models automatically and provide a format which is directly consumable by H2O MLOps.
H2O Hydrogen Torch speeds up detection and classification of abnormalities on medical images, such as patient X-rays.
Insurance
Combine image with sensor data to assess automobile damage and estimate severity
Identify actual with stock images to evaluate claims fraud and make next-step recommendations based on image classification
Manufacturing
Optimize development by detecting required pieces during assembly process
Analyze image and other data from sensors to detect efficiency
Customer Experience
Search for identical products in e-commerce websites to make product recommendations
Predict customer satisfaction from phone transcriptions and categorize incoming emails
Build unique, domain specific Q&A systems and find similar questions in user forums
Automotives and Self Driving Cars
Detect vehicles from traffic or drone cameras
Segment road based on video from dashcams
Classify landscape and drone photos and find matches within databases