AI systems can fail with every request returning 200. This guide covers detection signals, containment, provider failover, release rollback, and recovery verification, built around one question: which effects can be undone and which cannot.
A cheaper model call does not mean a cheaper result. This guide defines cost per successful task, models what retries, caching, routing, and human review do to it, and shows a simulation where cost per request falls while cost per success rises.
On a shared AI platform, tenant identity gets lost between the gateway, the index, the cache, and tool calls. This guide shows where, how to fix each path, and how to test isolation.
A healthy dashboard can sit above a model that gives wrong answers. This guide defines latency, availability, and task-success indicators, error budgets, and degradation for AI services.
AI debugging wants prompts and tool results, which are the most sensitive data a request holds. This guide shows what to record by default, how far redaction can be trusted, and how to control access.
When a model can act, the runtime around it decides what actually happens. This guide covers multi-tenant isolation tiers, short-lived delegated tool credentials, a policy engine, approvals bound to one call, idempotent execution, run state and resume, and a hash-chained action audit, with runnable code.
A prompt or model change should reach users only with evidence. This guide builds a release gate service: versioned evaluation sets, non-inferiority thresholds, paired bootstrap intervals, slice checks, judge calibration, approvals, canary stages, and rollback, with runnable simulations.
A model gateway is the single entry point between applications and model providers. This guide covers caller identity, routing, token-based limits and budgets, provider differences, and failover with audit, with a runnable gateway and fake providers.
A model server is a queue in front of an expensive machine. This guide explains how batching, replica scaling, latency targets, and overload policy determine tail latency, with runnable simulations and a benchmarking method that avoids coordinated omission.
A distributed training job stops when any one worker fails, and what it loses depends on its checkpoints. This guide covers the job lifecycle, distributed failure modes, what a checkpoint must contain, atomic writes, verified resumption, and choosing the interval with a waste model.
A GPU cluster can show free devices and still be unable to start a job. This guide covers GPU memory and compute, placement and fragmentation, gang scheduling, quotas with borrowing, sharing modes and their isolation limits, and capacity under failure, with local simulations.
A model version number says little about what the model was built from or what it needs to run. This guide builds a small lineage and registry to show which artifacts a change invalidates, what reproducibility requires, and how promotion and rollback depend on compatibility.
A shared AI platform works only when it is clear what the platform promises and what each consuming team still owns. This guide covers consumer types, contracts, ownership, tenancy, and the runtime and evaluation services, with a runnable contract checker.