CAIP (AIP-210) cheat sheet

Implementing and Handing Off an AI Solution or Environment

• SageMaker endpoint types: real-time (persistent, low latency), serverless (scale to zero, intermittent traffic), asynchronous (large payloads, long processing), and batch transform (offline bulk scoring).

• Deployment safety patterns: blue/green and canary deployments shift a fraction of traffic to the new model; A/B testing compares variants with statistical significance before full rollout.

• Monitoring: CloudWatch tracks invocation counts, latency, 4xx/5xx errors, and Model Monitor detects data drift, model-quality drift, bias drift, and feature-attribution drift.

• SageMaker Model Registry versions models with approval status (Pending/Approved/Rejected) and metadata, enabling governed promotion from dev to prod.

• Pipelines and reproducibility: SageMaker Pipelines or Step Functions orchestrate preprocessing, training, evaluation, and conditional registration; pin container images and record data/artifact lineage.

• Security: IAM execution roles with least privilege, VPC endpoints for private traffic, KMS encryption at rest, and interface VPC endpoints for SageMaker APIs.

• Cost control: use Spot instances for training, Savings Plans or Managed Spot for endpoints, delete idle endpoints, and right-size instance types based on load tests.

• Handoff documentation: model card (intended use, metrics, limitations), runbook (retraining triggers, rollback steps), and data/feature dictionary are minimum deliverables.