Only H2O.ai provides an end-to-end GenAI platform where you own every part of the stack. Built for airgapped, on-premises or cloud VPC deployments.
H2O.ai is praised for its robust capabilities in building machine learning models, particularly for creative and complex projects like recommender systems. Users often highlight its powerful AI and automation features that streamline data analysis and model deployment. However, there's limited feedback on specific user complaints or pricing concerns in the available mentions. Overall, H2O.ai maintains a positive reputation, especially in professional and developer communities, for its innovative AI solutions.
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2,034 forks
H2O.ai is praised for its robust capabilities in building machine learning models, particularly for creative and complex projects like recommender systems. Users often highlight its powerful AI and automation features that streamline data analysis and model deployment. However, there's limited feedback on specific user complaints or pricing concerns in the available mentions. Overall, H2O.ai maintains a positive reputation, especially in professional and developer communities, for its innovative AI solutions.
Features
Use Cases
Industry
information technology & services
Employees
330
Funding Stage
Series E
Total Funding
$246.1M
1,846
GitHub followers
257
GitHub repos
7,522
GitHub stars
5
npm packages
40
HuggingFace models
Another exciting 💡! Shrinidhi Narasimhan from Mumbai India created a recommender system for an e-commerce use case based on purchasing history using matrix factorization. She published her project on
Another exciting 💡! Shrinidhi Narasimhan from Mumbai India created a recommender system for an e-commerce use case based on purchasing history using matrix factorization. She published her project on the @h2oai blog and here: https://t.co/dOtgooeq7V https://t.co/F5N1IewYm0
View originalAnother exciting 💡! Shrinidhi Narasimhan from Mumbai India created a recommender system for an e-commerce use case based on purchasing history using matrix factorization. She published her project on
Another exciting 💡! Shrinidhi Narasimhan from Mumbai India created a recommender system for an e-commerce use case based on purchasing history using matrix factorization. She published her project on the @h2oai blog and here: https://t.co/dOtgooeq7V https://t.co/F5N1IewYm0
View originalRepository Audit Available
Deep analysis of h2oai/h2o-3 — architecture, costs, security, dependencies & more
H2O.ai uses a tiered pricing model. Visit their website for current pricing details.
Key features include: KYC and customer onboarding, Loan automation and fraud investigations, Trade reconciliation and regulatory reporting, Wealth portfolio rebalancing and debt collection, Call center resolution and customer support, Document routing and policy filing, Audio Surveillance Translation, Satellite Imagery Object Detection.
H2O.ai is commonly used for: Document routing and policy filing.
H2O.ai integrates with: Salesforce, Tableau, AWS, Azure, Google Cloud, Slack, Jupyter Notebooks, Alteryx, Power BI, Zapier.
H2O.ai has a public GitHub repository with 7,522 stars.
