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H2O-3 Core Platform

H2O-3 Core Platform

3
posts
2026

H2O Hydrogen Torch is an extension of the H2O-3 Core Platform, providing a no-code interface for state-of-the-art deep learning model training across various data modalities (text, image, 3D image, video, audio). It democratizes deep learning by enabling users to train, tune, and deploy models without coding, integrating with H2O MLOps for production deployment and H2O Wave for application development. Key capabilities include automated hyperparameter tuning, a library of problem types, and support for multi-GPU training.

2026

H2O Driverless AI

6/24/2026

This post introduces H2O Driverless AI, detailing its capabilities in automated feature engineering, model development, explainability, and deployment. It outlines the 'How it works' process: Connect, Transform, Build, Validate, Explain, Advise, and Deploy. It also highlights how Driverless AI addresses challenges for Data Scientists, DevOps/IT Professionals, and Business Analysts, emphasizing its role in democratizing AI adoption.

H2O Hydrogen Torch

6/24/2026

This post introduces H2O Hydrogen Torch, a new capability that democratizes deep learning by offering a no-code interface for training state-of-the-art models across text, image, 3D image, video, and audio data. It highlights features like automated hyperparameter tuning, a library of problem types, and seamless integration with H2O MLOps and H2O Wave for deployment and application development. It also details supported deep learning problem types such as classification, regression, object detection, segmentation, and sequence-to-sequence tasks.

H2O-3

6/24/2026

This post introduces H2O-3 as the foundational open-source distributed in-memory machine learning platform. It details its key features including algorithm support (GBM, GLM, Deep Learning, etc.), multi-language access (R, Python, Flow), AutoML capabilities, distributed in-memory processing, and easy model deployment (POJOs/MOJOs). It also outlines its integration with existing big data infrastructure (Hadoop, Spark, Kubernetes) and data sources (HDFS, Spark, S3, Azure Data Lake). Enterprise support options and use cases are also mentioned.