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 appreciated for its machine learning capabilities, particularly in creating sophisticated models such as recommender systems, as highlighted in social mentions. However, there is limited access to user reviews, so specific strengths and complaints are not clearly identified. The sentiment around pricing is not discussed in the available data. Overall, H2O.ai seems to have a positive reputation, especially in academic and developer circles where project sharing is common.
Mentions (30d)
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GitHub Stars
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2,034 forks
H2O.ai is appreciated for its machine learning capabilities, particularly in creating sophisticated models such as recommender systems, as highlighted in social mentions. However, there is limited access to user reviews, so specific strengths and complaints are not clearly identified. The sentiment around pricing is not discussed in the available data. Overall, H2O.ai seems to have a positive reputation, especially in academic and developer circles where project sharing is common.
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: Why H2O.ai, Products, Resources, Insights, KYC and customer onboarding, Loan automation and fraud investigations, Trade reconciliation and regulatory reporting, Wealth portfolio rebalancing and debt collection.
H2O.ai is commonly used for: Infrastructure and Operations, Claims Denials Management, Predictive Manufacturing Design, Next Best Offer, Assortment Optimization, Predictive Customer Support.
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.
