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Predictive AI and GenAI, Side by Side | H2O.ai Managed Cloud

H2O.ai13 August 2026Watch on YouTube

Part of series

Ep. 10 · Enterprise H2ogpte Part

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What you'll learn

  • H2O.ai Managed Cloud integrates predictive and generative AI on a single platform with unified governance, enabling organizations to deploy both AI types together
  • The platform is structured around three pillars: modelling and automation, AI applications and data engineering, and deployment, monitoring, and governance
  • Predictive AI forecasts what is likely to happen (fraud, demand, churn), while generative AI enables knowledge automation and reasoning
  • The platform handles both structured data (transactions, time series) and unstructured data (text, images, audio) in the same environment

Frequently asked questions

What predictive AI use cases are mentioned for H2O.ai Managed Cloud?
The video covers fraud detection, demand forecasting, credit risk assessment, churn prediction, and anomaly detection as examples of predictive AI use cases that run on the platform.
How does H2O.ai Managed Cloud ensure reliable AI systems in production?
The third platform pillar focuses on deployment, monitoring, and governance, providing secure implementation, scalable infrastructure, and continuous oversight of AI systems running in production.
What are examples of generative AI use cases covered on the platform?
The video covers enterprise assistants, document intelligence, summarization, retrieval-augmented generation (RAG), and agentic workflows as generative AI use cases.

Topics

platformpredictive-aigenerative-aienterprise-aideployment

Description from the channel

Fraud detection, demand forecasting, document intelligence, and retrieval-augmented generation all run on the same platform, under the same governance model. This lesson maps what you can actually build on H2O.ai Managed Cloud. The platform is structured around three pillars: modelling and automation, where models get built and refined; AI applications and data engineering, where models become systems people can use; and deployment, monitoring, and governance, where AI runs reliably in production. Within that structure, predictive AI answers what is likely to happen, and generative AI answers how you can generate, reason, and automate knowledge. Both work across structured data like transactions and time series, and unstructured data like text, images, and audio. Covered in this video: The three platform pillars and what each one is responsible for Predictive AI use cases: fraud detection, demand forecasting, credit risk, churn prediction, anomaly detection Generative AI use cases: enterprise assistants, document intelligence, summarization, RAG, agentic workflows How operationalization is built in, covering secure deployment, scalable infrastructure, monitoring, and governance LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #GenerativeAI #PredictiveAnalytics #H2Oai