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Deployment Examples

This section provides concrete examples of how the AI Product Deployment Specification (AIPDS) is applied to different product types.
Examples illustrate how deployment targets, packaging, monitoring, governance, and economics are declared in practice.

Example 1: LLM API for Enterprise Knowledge​

Use Case
A large language model (LLM) exposed as a secure enterprise API for question-answering across corporate knowledge bases.

Deployment Spec Highlights

  • Deployment Targets: Cloud (Azure), Edge (lightweight distilled version).
  • Packaging: REST API with OpenAPI spec; Python SDK.
  • Inference & Scaling: Online + streaming, autoscaling 1–100 replicas.
  • Monitoring: Latency, throughput, fairness (gender neutrality in outputs).
  • Retraining: Triggered retraining when accuracy falls below 92%.
  • Security: OAuth2 authentication, PBAC policies limiting use to approved domains.
  • Governance: Risk category High; prohibited for surveillance tasks.
  • Economic Model: $0.001 per token, sustainability disclosures quarterly.
  • Lifecycle State: Production.

Example 2: Edge Vision Model for Manufacturing​

Use Case
An AI Product deployed on factory floor cameras to detect defects in real time.

Deployment Spec Highlights

  • Deployment Targets: Edge (NVIDIA Jetson), On-Prem (factory servers).
  • Packaging: Docker container, ONNX model.
  • Inference & Scaling: Low-latency streaming ( < 30ms), supports 50 cameras.
  • Monitoring: Data drift detection, explainability via heatmaps.
  • Retraining: Monthly retraining on newly labeled defect images.
  • Security: Mutual TLS, encrypted edge-to-cloud communication.
  • Governance: Risk category Limited; compliance with ISO 13485 (medical devices).
  • Economic Model: Cost per device + licensing fee.
  • Lifecycle State: Development (moving toward Production).

Example 3: Multi-Agent Customer Support System​

Use Case
A set of AI agents working together to provide tiered customer support (FAQ, troubleshooting, escalation).

Deployment Spec Highlights

  • Deployment Targets: Agent runtime (LangChain + orchestration service).
  • Packaging: SDK with declarative workflow templates.
  • Inference & Scaling: Online + hybrid (batch log analysis + real-time chat).
  • Monitoring: Abuse detection (prompt injection, malicious inputs), fairness in ticket routing.
  • Retraining: Human-in-the-loop retraining with annotated customer feedback.
  • Security: OIDC auth, quotas per department.
  • Governance: Risk category Limited; audit logs retained for 180 days.
  • Economic Model: Per-conversation subscription pricing.
  • Lifecycle State: Production.

Example 4: Generative Design AI Product​

Use Case
A generative AI model that creates product design prototypes for industrial engineers.

Deployment Spec Highlights

  • Deployment Targets: Cloud (GCP), Hybrid (on-prem GPU clusters for IP-sensitive projects).
  • Packaging: API + containerized runtime; downloadable CAD-compatible model outputs.
  • Inference & Scaling: Batch + real-time; GPU scaling to 20 replicas.
  • Monitoring: Drift detection in style compliance; explainability for design rationale.
  • Retraining: Triggered retraining when user satisfaction drops below 85%.
  • Security: Access restricted to authenticated engineers; export controls enforced.
  • Governance: Risk category Limited; prohibited for weapons design.
  • Economic Model: Consumption-based (per design generated) with enterprise tiers.
  • Lifecycle State: Production.

Summary​

  • These examples demonstrate AIPDS in action across different categories: LLMs, vision models, agents, and generative tools.
  • Each product must declare deployment targets, packaging, inference, monitoring, retraining, security, governance, economics, and lifecycle state.
  • AIPDS ensures AI Products are transparent, auditable, and trustworthy in real-world deployment.

Principle: Examples bridge the gap between specification and practice — showing how AIPDS applies to diverse AI Products.