Explore H2O.ai's Self-Guided Tours

H2O.ai and Carahsoft have partnered together to provide a series of self-guided tours of H2O.ai's products and features. Similar to a live demo, the self-guided tours explores how H2O.ai's products and features applies to a specific technology vertical such as Artificial Intelligence.


Learn about H2O.ai's benefits, watch a short pre-recorded demo video, and download related resources. If interested in furthering the conversation, you can also schedule a live demo with a H2O.ai expert from Carahsoft. Start a Self-Guided Tour now by selecting one below: 


H2O.ai Artificial Intelligence Self-Guided Tour

H2O.ai Artificial Intelligence Self-Guided Tour

H2O.ai is a visionary AI and machine learning software company that offers a robust platform with built-in AI tools that help organizations quickly organize their data, build accurate, predictive models from that data, and find useful insights while still maintaining ethical AI usage standards. H2O.ai's platform equips the federal government with AI and machine learning tools for predictive modeling across sectors like healthcare and defense. It streamlines processes through automation, enhancing data analysis, and compliance while boosting cybersecurity by detecting anomalies and potential breaches. The platform's predictive analytics optimize resource allocation for diverse agency programs. Its customizable solutions cater to specific needs, enhancing decision-making across various government applications.

Want to dive further into H2O.ai's analytical tools and features?

Benefits Snapshot:


  • Deliver AI Initiatives 10x faster with our end-to-end platform, H2O Driverless AI.
  • Collaborations with Intel, NVIDIA, IBM, HPE, Dell, AWS, Azure, GitHub, and Google.
  • We focus on the “Why” – providing easily understandable and explainable reasoning for all of our model predications.
1 of 4

Enterprise Generative AI

Using a localized language model and vector database empowers you to retain data control and ensure privacy while leveraging potent language processing capabilities. An accessible solution, h2oGPT, found on GitHub, amalgamates these elements into a user-friendly installation package. It encompasses a substantial language model, an embedding model, a document embeddings database, a command-line interface and a graphical user interface. This tool accommodates various document types, such as plain text (.txt), comma-separated values (.csv), Word (.docx and .doc), PDF, Markdown (.md), HTML, Epub and email files (.eml and .msg).


  • Safety and privacy are ensured through the implementation of Guardrails and validation measures that detect and eliminate personally identifiable information (PII) or sensitive data, supported by response validations
  • Available for hosting within your private cloud or on-premises, this solution caters to Cloud VPC setups in AWS, Azure and GCP. It ensures dedicated hardware allocation, maintaining a single-tenant environment without hardware sharing among different cloud customers
  • RAG (Retrieval Augmented Generation) utilizing VectorDB, Embeddings and LLM for advanced data understanding and generation capabilities
2 of 4

H2O Driverless AI

H2O.ai's Driverless AI is an automated machine learning platform designed to simplify and accelerate the process of developing and deploying machine learning models. It automates various stages of the machine learning workflow, such as feature engineering, model selection, hyperparameter optimization and model interpretability. The goal is to enable users, even those without extensive data science backgrounds, to build highly accurate predictive models efficiently. Driverless AI employs advanced techniques like automatic feature engineering and model interpretability to produce models that can be easily deployed in various environments.


  • Intelligent feature transformation
  • Automated Model development
  • Comprehensive explain ability toolkit
  • Expert recommender system
3 of 4

Document processing involves converting an image into a digital format, enabling humans to interact with its information in diverse ways. Document AI integrates multiple machine learning fields, encompassing optical character recognition, natural language processing, handwritten text recognition, text extraction and other techniques. This fusion enables data validation, document analysis and assists humans in extracting meaningful information from these documents.


  • Document AI utilizes natural language processing, optical character recognition, deep learning and machine learning to capture, extract and process data from various document formats. This conversion of unstructured and semi-structured documents into usable data enables machine learning algorithms to make predictions.
  • This solution autonomously generates labels for documents lacking labels, rectifies data labeling errors within training data and offers an intuitive interface that streamlines the annotation process for both data scientists and business users. It seamlessly integrates with popular label formats and offers sophisticated features to validate labels against scored documents while ensuring labeling adequacy.
  • Delivers rapid and remarkably precise outcomes. Crafted through insights from esteemed Kaggle grandmasters and a wide array of clients, it leverages intelligent character recognition (ICR), optical character recognition (OCR) and natural language processing to extract vital information from documents, including entity names, logos, images, invoice numbers and beyond.
  • Seamlessly integrates into current applications and workflows through a REST API, facilitating document processing, model training and scoring. These models can be monitored and governed within H2O MLOps, offering a supervised scoring environment that manages A/B testing, logging, error handling and scaling. Leveraging the H2O Document REST API alongside H2O Wave allows for the swift development of AI applications, enabling the deployment of constructed models within H2O Document AI.
4 of 4


H2O MLOps fosters a collaborative environment, simplifying the management, deployment, governance and monitoring of machine learning models in production. Presently, numerous organizations encounter challenges transitioning from experimenting with AI to deploying production-ready AI models that drive significant business impact. Hurdles such as cumbersome manual processes, a lack of DevOps expertise or resources and the inability to monitor models as they potentially become less accurate or more biased over time hinder progress. H2O MLOps streamlines these complexities by offering an intuitive interface for end-to-end model management, streamlined deployments, automated scaling and comprehensive model monitoring that includes automated detection of deviations in accuracy and bias. Through H2O MLOps, organizations can swiftly transition AI models into production, continually enhancing them to ensure positive and responsible outcomes.


  • Automating the transition from experimentation to model deployment ensures a smooth, continuous process. H2O MLOps accelerates Data Science teams' ability to swiftly transition ML projects into production, condensing timelines from weeks to days and from days to hours.
  • Monitor models automatically in real-time and establish personalized thresholds to receive alerts concerning prediction accuracy and data drift. H2O MLOps guarantees the proper functioning of all deployed models, ensuring they perform as intended.
  • Minimize MLOps management through high-availability deployments and automated scaling, enabling Data Scientists to concentrate their efforts on creating valuable AI solutions.