16 docs tagged with "Enterprise, Kubernetes & OpenShift AI"

Check evaluation, authorization, sensitive logging, latency, cost, fallback and approvals before release.

Run datasets against model and prompt versions and compare thresholds before promotion.

Standardize latency, errors, saturation, model availability, cost and quality signals.

Review provider inventory, cost attribution, evaluation, logging, fallback, ownership and operations.

Retain usage, map costs to teams and combine tokens, APIs and GPU allocation.

Provide a reusable GitOps blueprint for authenticated, observable and evaluated AI applications.

Track allocatable GPU, reservations, workload ownership and stranded capacity.

Analyze manifests, security, storage, ingress, networking, policies and CI/CD assumptions.

Estimate memory, replicas, throughput, latency and hosted-versus-local economics.

Implement approved models, subscriptions, API keys, quotas, authorization and developer onboarding using native capabilities.

Assess cluster, GPU, storage, networking, identity, quotas, monitoring, model storage and workload isolation.

Create a normalized package from events, state, telemetry, GitOps changes, commits and runbooks.

Identify over-reservation, low utilization, missing limits and capacity candidates.

Identify API removals, Operator compatibility, manifest risks, owners and remediation tasks.

Assess VM workloads, networking, storage and migration constraints.

Analyze GitOps changes, policy conflicts, affected workloads, capacity and observability.