Uploaded August 2026 | Updated September 2026, 1 week ago
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: h2o.ai/university
Documentation: docs.h2o.ai
Request access or a demo: 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
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: h2o.ai/university
Documentation: docs.h2o.ai
Request access or a demo: 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
