Model Deployment

## Purpose

Operational guidance for packaging, versioning, and deploying models (LLMs, fine-tuned variants, or ML artifacts) with reproducibility and observability.

## Implementation Patterns

### Pattern 1: Model Registry & Artifact Metadata
Store model artifacts with semantic versioning, hash checksums, runtime requirements, and provenance metadata.

Key steps:
- Publish artifacts to a registry with `model_name:version` and metadata (framework, tokenizer, quantization).
- Store deployment manifests and rollout plans alongside the artifact.

### Pattern 2: Canary + Shadow Deployments
Use canary rollouts and shadow traffic to validate new versions under realistic load without affecting production users.

Key steps:
- Deploy new model to subset of inference nodes (5%).
- Route a copy of production traffic (shadow) and compare outputs offline.
- Monitor metrics (latency, error rate, quality metrics) and promote on success.

### Pattern 3: Automated Rollback & Validation
Automate health checks and rollback conditions to minimize downtime after a degrading deployment.

Key steps:
- Define SLOs (latency, error rate, accuracy) and thresholds for rollback.
- If new model breaches thresholds, trigger automatic rollback to previous stable version.

## Examples

1) Canary deploy flow (pseudo):