Video by H2O.ai via YouTube
How H2O.ai orchestrates enterprise AI workloads on Kubernetes with managed resource profiles and cost guardrails.
As AI programs scale, managing compute resources across teams and use cases becomes operationally critical. H2O.ai runs all workloads—Driverless AI experiments, Feature Store operations, MLOps deployments, and h2oGPTe agent executions—as managed Kubernetes workloads. Administrators define specialized resource profiles allocating the right CPU, GPU, and memory per task. Cost guardrails enforce idle timeouts, maximum run durations, and dynamic cluster autoscaling, keeping infrastructure spend under control without requiring Kubernetes expertise from data scientists.
Technical Capabilities & Resources
➤ Workload Orchestration & Resource Profiles: Schedule ML workloads using admin-managed profiles that allocate CPU, GPU, and memory automatically.
🔗 https://docs.h2o.ai/ai-engine-manager/user-guide/dai-engine/create-dai-engine/#step-4-configure-resources
➤ Cost Optimization & Infrastructure Guardrails: Control compute costs with resource constraints, idle timeouts, and dynamic cluster autoscaling.
🔗 https://docs.h2o.ai/mlops/model-deployments/create-a-deployment#advanced-settings
➤ H2O Engine Management: View and manage engine configuration and last-used resource profile information.
🔗 https://docs.h2o.ai/ai-engine-manager/user-guide/h2o-engine/manage-h2o-engine/