AI Model Deployment Checklist

Passing evaluation is necessary and not sufficient. Models fail in deployment through feature skew, unhandled load, provider timeouts, and rollbacks that were never tested. This checklist covers what must be true before a model or LLM configuration takes production traffic. It draws on how to build a ci-cd pipeline for machine learning, how to build a model registry, and what is model deployment.

Who should use this checklist?

Engineering and MLOps teams deploying models or LLM configurations, and reviewers approving production changes.

Is the version registered and approved?

1. The version exists in the model registry with immutable artifact or configuration reference.
2. Lineage links to code commit, data snapshots, feature versions, prompt versions, and base model version.
3. Evaluation results by category, safety tests, and comparison to production are attached and pass thresholds.
4. Fairness testing is attached where applicable.
5. Approval is recorded by the authorized role for the risk tier.
6. Deployment tooling pulls only from the registry.

Reference: how to build an ai quality gate.

Is training-serving parity verified?

1. Features and preprocessing at serving match training, verified by computing both paths on a sample.
2. A feature store or shared transformation code serves both paths.
3. Schema and type contracts for inputs are enforced at the serving boundary.
4. For LLM configurations, prompt rendering and retrieval in serving match what was evaluated.

Reference: how to build a feature store.

Does the serving path meet targets?

Check	Evidence
Latency budget met at projected peak	Load test report
Throughput and autoscaling behavior verified	Load test report
Timeouts, retries, and fallbacks implemented and tested	Failure injection results
Provider outage and rate-limit handling tested	Failure injection results
Resource limits and cost per request within budget	Cost model and test
Idempotency for write actions	Test results

Reference: how to build an llm gateway and the LLM production readiness whitepaper.

Is the rollout progressive?

1. A rollout strategy is chosen by consequence: shadow, canary, blue-green, or A/B.
2. Success and abort criteria for each stage are defined on behavioral metrics, not only health.
3. Traffic split mechanics and duration are documented.
4. Comparison against the current version is instrumented.

Reference: what is a canary deployment and what is a shadow deployment.

Is monitoring live before traffic?

1. Operational metrics: latency, errors, throughput, fallback rate, cost.
2. Quality metrics: sampled scoring, confidence distributions, escalation rates.
3. Drift indicators with baselines from evaluation.
4. Alerts route to named owners with runbooks.
5. Monitoring data is attributed to the version.

Reference: the AI observability whitepaper.

Is rollback ready?

1. The previous approved version remains deployable.
2. Rollback is a pointer change executable in minutes.
3. Automatic rollback triggers are defined for error, latency, and quality thresholds.
4. Rollback has been exercised recently.
5. Data or state changes that rollback cannot undo are identified with mitigation.

Is documentation complete?

1. Model or system card with intended use, limitations, evaluation summary, and version.
2. Runbooks for common failures and rollback.
3. Change notes describing what changed and why.
4. Owners named and on the escalation path.
5. Consumers notified of behavioral changes where relevant.

Reference: what is a model card.

Are security and compliance settled for this version?

1. Credentials and permissions unchanged or reviewed if changed.
2. Data handling consistent with classification and provider terms.
3. Adversarial tests included in the evaluation results.
4. Regulatory documentation updated where the system is in a regulated tier.

Reference: the LLM security checklist.

Is the post-deployment review scheduled?

1. A review date after full rollout is set.
2. Metrics to review and decision criteria for keeping, tuning, or rolling back are defined.
3. Production failures will be imported into the evaluation suite.

How should failing items be handled?

Registry, parity, and rollback failures block deployment. Serving target failures block beyond shadow mode. Documentation gaps block full rollout. Record decisions with owners and dates in the registry.

Worked example: a provider model upgrade

A team adopts a provider's new base model for a support assistant. The new configuration is registered as a candidate with the pinned model version and unchanged prompts, evaluation runs by intent and passes with a small improvement, and a load test shows latency within budget. Rollout starts as a shadow comparison for two days, moves to a ten percent canary with quality sampling, and is promoted after the abort criteria stay clear. The previous configuration remains one pointer change away, which is exercised in the monthly rollback drill the following week.

How FISTA Solutions deploys models

FISTA Solutions deploys models and LLM configurations through pipelines that enforce this checklist: registry-only deployment, verified parity, load and failure testing, progressive rollout with behavioral metrics, tested rollback, and documentation. The AI enablement practice delivers the pipeline and registry, AI agents ship through them, and forward deployed engineers integrate them with your delivery tooling. The record behind the approach is 150+ projects with 99.9% uptime.

To review a deployment against this checklist, message FISTA on WhatsApp, or read mlops services for the operating capability behind it.