MODEL REGISTRY ARCHITECTURE
A model registry stores trained model artifacts along with metadata including hyperparameters, training data hash, evaluation metrics, and GPU hardware configuration. MLflow Model Registry leads adoption at 62 percent of enterprise AI teams, followed by custom solutions at 22 percent and SageMaker Model Registry at 12 percent. Each model version stores the serialized model weights, tokenizer configuration, and inference signature in S3-compatible object storage.
Model artifacts for LLMs consume significant storage. A Llama 3 70B model in FP16 occupies 140 GB per version. With 10 versions across staging, canary, and production, storage reaches 1.4 TB per model family. Deduplication via content-addressed storage reduces this by 60-70 percent when versions share common weight initialization.
| Registry Feature | MLflow | SageMaker | Custom | Critical for GPU Deployments |
|---|---|---|---|---|
| Model versioning | Yes | Yes | Yes | Yes |
| Lineage tracking | Automatic | Manual | Custom | Yes: reproduce results |
| Deployment transitions | Manual stages | CI/CD integration | Full control | Yes: canary flow |
| Model signature | Fixed schema | Inferred | Custom | Yes: input validation |
| GPU hardware labels | Custom tags | Auto-detected | Custom | Yes: deployment targeting |
| Large model support | < 10 GB limit | Native | Unlimited | Yes: LLMs are 140+ GB |
MODEL VERSIONING AND PROMOTION STRATEGIES
Model versioning follows semantic conventions: major version for architecture changes, minor for retraining with new data, patch for quantization or optimization. A production LLM might reach v3.2.5 with 8 patch versions for FP8 quantization tuning. Each version stores evaluation metrics: MMLU score, HumanEval pass rate, and latency benchmarks on H100 and A100.
Promotion gates require validation at each stage. Development stage requires MMLU within 1 point of baseline. Staging requires P99 latency under 200ms on H100 with FP8 and throughput above 1,000 tok/s. Production requires A/B test lift with statistical significance at p < 0.05 over minimum 5,000 requests. Only 18 percent of candidate versions pass all gates to production.
LINEAGE TRACKING AND GOVERNANCE
Complete lineage tracking records training data version, code commit hash, hyperparameters, GPU configuration, and evaluation metrics for each model version. This enables exact reproduction of any production model. For regulated industries like healthcare and finance, lineage must be immutable with cryptographic attestation using signed metadata.
Governance policies enforce deployment constraints. Models trained on H100 cannot be automatically deployed to A100 without requantization validation. Government classification models require approval from two senior ML engineers. Automated policy enforcement using Open Policy Agent checks these rules at promotion time, reducing governance violations by 88 percent compared to manual review.
CI/CD INTEGRATION FOR MODEL DEPLOYMENT
CI/CD pipelines for model deployment parallel software deployment patterns. A model pipeline includes: automated evaluation on holdout set, performance benchmarking on target GPU hardware, security scan for pickle/cPickle code execution risks, and canary deployment to 5 percent of traffic. Deployment rollback must complete within 30 seconds using prior model version.
GitHub Actions and GitLab CI integrate with MLflow via REST API. A typical pipeline runs after model registration: trigger evaluation, deploy to staging, run 1,000-request benchmark, promote to canary if benchmarks pass, rollout to production over 15 minutes with 5 percent traffic increments. Pipeline runtime averages 22-35 minutes.