Deployment Strategies

Pick infrastructure based on latency, cost, and isolation needs:

• Kubernetes: great for GPU scheduling, autoscaling, and complex topologies.
• Managed inference services: lower operational overhead for common ML workloads.
• Serverless / Functions: useful for fast stateless tasks but watch cold-starts for large models.
• Hybrid: serve real-time inference on GPUs, use CPU-based serverless for lightweight pre/post-processing.

Common patterns:

• Single-model service: host one model per service for isolation and clear scaling.
• Multi-model router: a front-line router that forwards requests to model workers (useful for many small models).
• Model shards / parallelism: shard very large models across GPUs or use model parallel runtimes.
• Edge portions: run lightweight preprocessing / caching at the edge; keep heavy inference centralized.

Minimize user impact when deploying model or infra changes:

• Canary releases: route a small percentage of traffic to the new version and monitor metrics.
• Blue/Green: keep two identical environments and switch traffic when ready.
• Shadowing: mirror production traffic to a new version for offline comparison without affecting users.
• Feature flags: control model features or behaviors without redeploying code.

• Automate rollback on key metric regressions (latency, error rate, conversion).
• Use health checks and readiness probes to avoid routing traffic to unhealthy pods.
• Keep previous model binary and infra config readily available for quick rollback.

Integrate post-deploy validations and automated canary analyzers to reduce manual intervention.

Autoscaling for AI apps requires attention to warm-up and GPU utilization:

• Use warm pools to avoid cold-start latency for large models.
• Scale on request queue length, GPU utilization, or custom metrics (e.g., pending batch size).
• Prefer gradual scale-up to avoid overshooting and unnecessary cost spikes.

• Store keys and secrets in a secrets manager (Vault, AWS Secrets Manager, etc.).
• Restrict access to model artifacts and logs; use IAM and network policies.
• Record provenance metadata (model version, seed, training data version) for reproducibility and audits.

Instrument the full request path and define SLOs (latency, error rate, business metrics):

• Collect p50/p95/p99 latency for encoder, model, and post-processing stages.
• Track model-specific quality metrics (e.g., accuracy, conversion uplift) if applicable.
• Store traces and sample request payloads (sanitized) to debug issues quickly.

Operational readiness includes:

• Automated integration & load tests that mimic production traffic patterns.
• Chaos testing (node termination, network latency) in staging to validate resiliency.
• Runbooks for common failures (high latency, OOMs, model misbehavior) with clear rollback steps.

CI/CD pipelines should validate model artifacts, run smoke tests, and trigger controlled deployments:

# Example (pseudo GitHub Actions)
- name: Validate model artifact
  run: python ci/validate_model.py --model artifacts/model-v2

- name: Build container
  run: docker build -t registry/myapp:model-v2 .

- name: Push & deploy canary
  run: |
    docker push registry/myapp:model-v2
    kubectl apply -f k8s/canary.yaml

Integrate automated metric checks after canary deploy and gate promotion on success.