AI TOOL PROFILE
H2O.ai Machine Learning and AI Platform
- Data and Analytics
- Machine Learning Platform
- Enterprise companies
- Data science teams
- ML engineers
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Data science teams, ML engineers
- Key use cases
- AI/ML Application Development, Model Operationalization, Automated Model Building, LLM Fine-Tuning
- Integrations
- Spark, Hadoop/Yarn
- Official website
- Visit H2O.ai Natural Language Processing (NLP) official website
How AI is used
H2O.ai provides a suite of tools for the machine learning lifecycle. The platform includes H2O AI Cloud, which integrates components such as a feature store, MLOps tools, and notebook labs to support the transition from model development to production deployment.
The ecosystem supports users through H2O-3, an open-source platform, and H2O Driverless AI, which is designed to automate parts of the model-building process.
For generative AI needs, the platform includes H2O LLM Studio, a framework and no-code GUI for fine-tuning large language models. This allows organizations to combine predictive analytics with LLM capabilities.
Buyers should confirm their team's technical proficiency, as the platform is intended for users with high technical skill levels.
Key Features
H2O Driverless AI
An automatic machine learning platform that supports automated feature engineering, model building, and visualization.
H2O LLM Studio
A no-code GUI and framework designed for fine-tuning large language models.
H2O MLOps
Supports model deployment, scoring, and monitoring of models in production.
H2O Feature Store
An API-driven system for managing and versioning machine learning features.
H2O-3
A distributed in-memory open source machine learning platform supporting R, Python, and Scala.
H2O Wave
A framework for building AI/ML applications and dashboards using Python and R.
Use Cases
AI/ML Application Development
Using integrated platform components to build and test machine learning models.
Model Operationalization
Deploying models into production environments with monitoring and scoring via MLOps tools.
Automated Model Building
Using automatic machine learning to handle feature engineering and visualization.
LLM Fine-Tuning
Customizing large language models using the LLM Studio framework.
Integrations
- Spark
- Hadoop/Yarn
FAQ
Who is H2O.ai designed for?
- It is primarily designed for software companies and enterprise-level organizations with high technical capabilities.
Does H2O.ai support open-source options?
- Yes, H2O-3 and H2O Wave are open-source products licensed under the Apache License 2.0.
Can H2O.ai be used for Generative AI?
- Yes, the platform includes H2O LLM Studio, which provides a no-code GUI for fine-tuning large language models.
Source category: Data & Analytics
Source subcategory: Machine Learning Platform
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