H2O.ai Feature Trails

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H2O Danube Large Language Models

H2O LLM Studio provides a no-code framework for fine-tuning state-of-the-art Large Language Models (LLMs) and Small Language Models (SLMs). It leverages Deepspeed for distributed training on GPU clusters, enabling cost-effective and faster training compared to traditional LLMs. The studio supports various fine-tuning techniques including instruction/chat fine-tuning, causal classification and regression, and DPO/IPO/KTO optimization. It also facilitates distilling LLMs into SLMs using H2O LLM Danube. This post highlights the performance of H2O-Lightning-4B on JevBench, demonstrating its state-of-the-art capabilities.

4 posts · 2026

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Agentic AI Platform for Telecom

The Agentic AI platform integrates Generative AI with Predictive AI for telecommunications, offering specialized AI agents for workflows like contact center automation, SQL query generation, churn prediction, and billing issue resolution. The platform supports sovereign AI deployments to streamline operations, boost efficiency, and enhance customer experiences, with case studies showing significant cost reductions and ROI improvements. This post details the application of this platform in the ba

2 posts · 2026

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Document AI

H2OVL Mississippi is a new multimodal foundation model family for OCR and Document AI use cases. H2OVL Mississippi-0.8B and 2B models offer strong performance in text recognition and image understanding, with the 0.8B model outperforming larger models on OCR benchmarks. The architecture is inspired by LLaVA and InternVL, utilizing a ViT-MLP-LLM setup with dynamic resolution and multi-scale adaptive cropping. Training involves a two-stage methodology with pretraining and fine-tuning stages tailored for document understanding. The Document AI platform integrates OCR, NLP, and deep learning to extract information from forms and unstructured text, enabling automated data labeling, intelligent information extraction, and seamless integration with existing workflows via REST APIs. Models can be deployed to H2O MLOps for monitoring and management, or to custom environments. The platform supports ingestion, labeling, training, deployment, and consumption of document data, transforming business operations by automating repetitive tasks and freeing up human resources for higher-value work. It caters to Data Scientists by simplifying the delivery of document processing pipelines and to Business Users by automating lower-level tasks and highlighting relevant information.

2 posts · 2026

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H2O Feature Store

The H2O Feature Store is a system designed to extract, manage, and optimize the feature engineering and model building process for machine learning. It provides a central repository to connect information across disparate systems, enabling reuse of data and feature engineering efforts. Key capabilities include data versioning, lineage tracking, encryption for security and privacy, operational efficiency through API integration, proactive feature recommendations, and automated monitoring for data quality and drift. It also supports backtesting and time travel for model validation, and integrates human feedback loops for bias identification and drift detection.

1 post · 2026

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H2O Label Genie

H2O Label Genie is a system designed to accelerate data annotation for machine learning by leveraging AI. It supports various data types (text, images, video, audio) and deep learning tasks (classification, regression, object detection, instance segmentation, entity recognition, summarization). It can be deployed as a managed or hybrid solution and is SOC2 compliant. It integrates with H2O Hydrogen Torch for model training and H2O MLOps for deployment. This post highlights its capabilities in accelerating data annotation workflows by using AI to automate or speed up the process, offering a 10x speedup or full automation. It supports both real-time and batch applications, and can handle extremely large models like LLMs as well as tiny AI models for edge devices. The system is SOC2 compliant and offers managed or hybrid deployment options.

1 post · 2026

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H2O MLOps

H2O MLOps provides a comprehensive platform for managing, deploying, governing, and monitoring machine learning models in production. It streamlines the end-to-end model lifecycle, from experimentation to production deployment and ongoing monitoring. Key capabilities include model management and registry, 3rd party model ingestion, collaborative experiment repositories, model versioning, various deployment modes (real-time, batch, A/B testing), multi-environment support, automated scaling, and model monitoring with drift detection for accuracy and bias. The platform also emphasizes team collaboration, governance, compliance, and provides flexible infrastructure options with high availability and Kubernetes configuration.

3 posts · 2026

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H2O Sparkling Water Integration

H2O Sparkling Water integrates H2O's machine learning algorithms with Apache Spark, enabling users to perform Spark SQL queries, build models with H2O, and use the results within Spark. It supports computation from Scala, R, and Python, and allows for easy deployment of H2O models (POJOs/MOJOs) for scoring. The system runs as a regular Spark application, initializing H2O services on Spark and accessing data from both Spark and H2O data structures. It offers advanced machine learning for Spark and integrates with cloud platforms like Azure, AWS, and Google Cloud.

1 post · 2026

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H2O Wave AI App Development Framework

H2O Wave is an open-source Python development framework for building real-time interactive AI applications. It provides a rich set of UI components and charts, enabling developers to create sophisticated visualizations and dashboards without requiring HTML, CSS, or JavaScript expertise. Wave's real-time server facilitates streaming dynamic information, and it integrates seamlessly with H2O's machine learning platform and AI Cloud for model building and deployment. The framework supports deployment across various cloud environments and major operating systems, and integrates with popular ML libraries like TensorFlow, scikit-learn, PyTorch, and Pandas.

2 posts · 2026

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H2O-3 Core Platform

H2O Hydrogen Torch is a no-code deep learning platform for training state-of-the-art models across various data types (text, image, 3D image, video, audio). It democratizes deep learning by allowing users to experiment with hyperparameters, tune models, and achieve high accuracy without coding. Models can be deployed to H2O MLOps or external Python environments. The platform supports a wide range of deep learning problem types including classification, regression, object detection, semantic segmentation, instance segmentation, and metric learning. It integrates with H2O Wave for app development and APIs for broader consumption.

5 posts · 2026

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Foundation Models for Tabular Data

tabH2O is a unified foundation model for tabular prediction, handling classification and regression tasks with a dual-head architecture. It features single-stage pretraining with stability improvements (bounded scalable softmax, inter-stage normalization, learnable residual scaling, logit soft-capping) and noise-aware pretraining for robustness to irrelevant features. This post announces its unveiling at Dell Technologies World 2026.

3 posts · 2026

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