AI Deployment

Deployment Strategy

Production Deployment Patterns: Define standard patterns for deploying models (batch, real-time, streaming) aligned to latency, reliability, and cost needs.
Environment Strategy: Design dev/test/stage/prod environments, promotion rules, and separation of duties to reduce release risk.
Release Gate Design: Establish measurable gates for quality, security, risk, and performance before production promotion.
Go-Live Readiness Criteria: Create objective go/no-go criteria across data readiness, monitoring coverage, support readiness, and rollback plans.

Model Artifact Standardization: Standardize how models, prompts, configs, and dependencies are packaged for repeatable deployment.
Versioning Strategy: Implement versioning for models, datasets, prompts, and retrieval indexes to make changes traceable and reversible.
Reproducible Builds: Ensure builds are deterministic with locked dependencies and documented runtime requirements.
Model Registry Setup: Establish a registry for lineage, approvals, metadata, and promotion status across environments.

Automated Test Pipelines: Integrate unit, integration, and regression tests into automated pipelines for every change.
Promotion Workflows: Automate promotion across environments with approvals, audit logs, and artifact integrity checks.
Infrastructure as Code: Manage infrastructure changes through code and reviews to reduce configuration drift.
Rollback Automation: Enable rapid rollback to prior versions with minimal downtime and clear triggers.

Serving Architecture: Design serving stacks for throughput, latency, autoscaling, and high availability.
Hardware Strategy: Decide GPU/CPU allocation, pooling, and scheduling to optimize cost and performance.
Multi-Region Resilience: Implement redundancy and failover options appropriate to criticality and SLA needs.
Capacity Planning: Forecast load and plan capacity using demand models, usage baselines, and growth scenarios.

API Design Standards: Standardize AI service interfaces (inputs, outputs, error handling) to simplify consumption by applications.
Event and Workflow Integration: Integrate AI into queues, orchestration tools, and business workflows without brittle point-to-point coupling.
Data Access Integration: Build secure access paths to required data sources with stable contracts and least-privilege permissions.
Identity and Permission Integration: Ensure user identity, authorization, and entitlements flow through to AI decisions and retrieval.

Model Performance Monitoring: Monitor prediction quality, calibration, and key error types aligned to business impacts.
GenAI Output Monitoring: Track grounding, refusal rates, policy violations, and answer quality trends for genAI systems.
Data Drift Detection: Detect distribution shifts, schema changes, and missingness that degrade model behavior.
Alerting and Triage: Define actionable alerts, severity levels, and triage workflows to prevent “dashboard-only” monitoring.

SLA and SLO Definition: Define reliability targets (latency, uptime, error rate) and align ownership for achieving them.
Failure Mode Analysis: Identify likely failure modes (provider outages, retrieval failures, bad inputs) and design mitigations.
Graceful Degradation: Implement fallbacks (cached results, smaller models, rules) to maintain service during partial failures.
Load and Stress Testing: Validate performance under peak load and adversarial usage patterns before scaling.

Secrets and Key Management: Implement secure handling of API keys, tokens, and credentials across environments.
Network Controls: Apply segmentation, private connectivity, and egress controls appropriate to data sensitivity.
Secure Logging: Ensure logs capture necessary diagnostics without leaking sensitive inputs, outputs, or retrieved content.
Vulnerability Management: Scan dependencies and images, patch rapidly, and manage CVEs for AI runtime components.

Inference Cost Instrumentation: Track cost drivers (tokens, calls, GPU minutes) by product, workflow, and user segment.
Routing and Caching: Use smart routing, caching, and summarization to reduce cost while preserving quality.
Budget Guardrails: Implement quotas, rate limits, and alert thresholds to prevent runaway spend.
Cost-Quality Tradeoffs: Establish decision rules for when to use premium models versus cheaper alternatives.

Runbook Development: Create runbooks for common incidents, degraded modes, and escalation paths.
On-Call Model: Define ownership, rotation, and escalation for AI services aligned to criticality.
Incident Response: Establish incident processes for outages, harmful outputs, leakage events, and vendor disruptions.
Post-Incident Reviews: Run blameless postmortems and convert learnings into prioritized fixes and controls.

Retraining Triggers: Define when to retrain or refresh models based on drift, performance decay, or business changes.
Prompt and Index Change Control: Govern prompt updates and retrieval index changes with testing, approvals, and rollbacks.
Deprecation Strategy: Retire models safely with comms, migration plans, and dependency discovery.
Continuous Improvement Cadence: Establish a cadence to refine models, evals, monitoring, and controls post-launch.