H2O Managed Cloud: Full Platform Overview
- H2O AI Managed Cloud
- H2O Driverless AI
- H2O Cascade
- Enterprise h2oGPTe
This lesson walks through what H2O Managed Cloud is and the problem it solves, since fragmented tooling across data, model building, and deployment causes delays and governance gaps. It covers the two principles behind the platform: managed, meaning H2O runs the infrastructure and GPU compute for you, and isolated, meaning each customer's environment and data are never shared with another. It shows where H2O Driverless AI, H2O LLM Studio, and Enterprise h2oGPTe each sit within the platform, covering automated machine learning, generative AI, and model operations in one place.
Key takeaways
- Why fragmented tooling across data, model building, and deployment causes delays and governance gaps
- What managed means for H2O Managed Cloud, with H2O running the infrastructure and GPU compute for you
- What isolated means, keeping each customer's data, compute, and workloads separate from other customers
- How H2O Driverless AI, H2O LLM Studio, and Enterprise h2oGPTe each fit within the unified platform
- The platform brings automated machine learning, generative AI, and model operations into one environment
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