Chapter 10: Deployment and Engineering

Exam focus

• API integration
• Model serving
• Microservices architecture
• REST / gRPC
• CI/CD for AI
• Containerization
• Edge deployment

Scope Bullet Explanations

• API integration: Expose model behavior safely and consistently to clients.
• Model serving: Host versioned models with predictable runtime behavior.
• Microservices: Separate concerns for scale, resilience, and ownership clarity.
• REST/gRPC: Interface tradeoffs by latency, typing, and ecosystem fit.
• CI/CD for AI: Automate validation and release with model-aware checks.
• Containerization: Reproducible runtime packaging.
• Edge deployment: Operate under constrained compute/network conditions.

Chapter overview

This chapter turns GENM models into production systems. Exam scenarios often test deployment architecture decisions, release controls, and operational reliability tradeoffs.

Assumed foundational awareness

Expected baseline:

• API contract basics,
• versioning principles,
• deployment pipeline stages.

Learning objectives

• Design serving architecture for multimodal model APIs.
• Choose interface protocols aligned to workload needs.
• Implement safe release workflow with rollback support.
• Evaluate edge deployment constraints and fallback strategy.

10.1 Serving architecture blueprint

Core components:

• API gateway and auth layer,
• preprocessing service,
• model inference service,
• postprocessing/policy layer,
• telemetry and tracing,
• model registry/version control.

10.2 Protocol and contract choices

REST

Simple and ubiquitous; useful for broad client compatibility.

gRPC

Efficient binary protocol with typed schemas and streaming support; useful for low-latency service meshes.

Protocol choice should match client ecosystem, SLA, and operability needs.

10.3 CI/CD for AI systems

AI release pipeline should include:

1. unit/integration tests,
2. model artifact validation,
3. evaluation gate checks,
4. canary rollout,
5. automated rollback triggers.

10.4 Containerization and environment stability

Containers reduce environment drift and enable reproducible deployments. Use immutable artifact versioning and dependency pinning for traceability.

10.5 Edge deployment considerations

Edge constraints include:

• limited memory/compute,
• intermittent connectivity,
• update management,
• privacy/offline requirements.

These often require smaller models and stricter runtime budgets.

Common failure modes

• Updating model without API contract compatibility checks.
• No canary or rollback controls in production rollout.
• Weak observability for cross-service failures.
• Treating edge as cloud-lite without constraint redesign.

Chapter summary

Deployment is a systems discipline. Exam readiness comes from demonstrating architecture choices that preserve reliability, security, and maintainability under real constraints.

Mini-lab: deployment release plan

1. Draft model-serving architecture for one GENM use case.
2. Define REST or gRPC contract.
3. Add CI/CD gates and rollback trigger rules.
4. Include one edge deployment variant.

Deliverable:

• release architecture + rollout checklist.

Review questions

1. Why do model and API versions both need tracking?
2. When is gRPC likely better than REST?
3. What is a minimum safe canary strategy?
4. How does containerization improve reproducibility?
5. Why should rollout gates include model metrics?
6. What edge constraints most commonly break cloud assumptions?
7. Why is observability central to microservice debugging?
8. How can dependency drift cause silent production regressions?
9. What belongs in a deployment rollback plan?
10. Why must security checks be integrated into CI/CD rather than post-release?

Key terms

Model serving, API contract, canary deployment, rollback, containerization, edge inference, CI/CD.

Exam traps

• Treating deployment as a single-step publish action.
• Ignoring interface compatibility and version governance.
• Shipping without measurable rollback criteria.