Model Archival

The core of the archive is the immutable model artifact—the serialized file containing the trained model's weights and architecture. This is the definitive, versioned binary that can be reloaded to reproduce exact inference behavior. Common formats include:

• ONNX (Open Neural Network Exchange) for framework-agnostic serialization.
• SavedModel for TensorFlow models.
• Pickle files for scikit-learn or PyTorch models (with dependency caveats).
• MLmodel format used by MLflow.

This artifact must be stored with a cryptographic hash (e.g., SHA-256) to guarantee its integrity and prevent tampering.

This component provides the essential context for understanding the model's purpose and limitations. It includes a Model Card, a standardized document detailing:

• Intended Use & Limitations: Authorized domains and known failure modes.
• Performance Metrics: Validation scores (accuracy, F1, etc.) across key slices and benchmarks.
• Training Configuration: Hyperparameters, optimizer settings, and loss functions.
• Creator & Date: Model author and creation timestamp.
• Ethical Considerations: Bias assessments and fairness evaluations.

This metadata is typically stored in a structured format like JSON or YAML for machine readability.

An audit trail that traces the model's origin. This is critical for compliance (e.g., GDPR, EU AI Act) and debugging. It documents:

• Training Data Provenance: Versioned datasets, preprocessing scripts, and feature engineering code.
• Code & Environment: Git commit hashes for training code and a snapshot of the runtime environment (e.g., conda-environment.yml, requirements.txt, or a container image digest).
• Experiment Tracking Links: References to runs in tools like MLflow, Weights & Biases, or Neptune, capturing the full experimental history.
• Approval Workflow Logs: Records of any governance sign-offs required before archival.

Defines the exact contract for how to use the model. This ensures the archived model can be correctly invoked in the future, even if the original serving code is lost. It includes:

• Input/Output Schema: Formal specification of feature names, data types, value ranges, and the structure of the prediction output (e.g., a JSON Schema).
• Inference Code/API Definition: The minimal code or a Protocol Buffer (.proto) file defining the serving API. For containerized models, this includes the Dockerfile used to build the serving image.
• Example Payloads: Sample valid and invalid inference requests and responses for documentation and testing.

A self-contained test suite to verify the model's functionality and performance post-retrieval. This is key for reproducibility. It contains:

• Unit Tests: Scripts to validate that the model loads and produces expected outputs for a fixed set of inputs.
• Performance Benchmark: A small, representative holdout dataset and a script to recalculate key metrics, confirming the archived model matches its reported performance.
• Drift Baseline Data: A statistical summary (e.g., mean, standard deviation, histograms) of the training data distribution to serve as a baseline for future data drift detection if the model is revived.

Administrative records governing the archival process and its legal context. This includes:

• Retirement Justification: Documented reason for archival (e.g., superseded by a new model, end-of-life for the product).
• Legal Hold Flags: Indication if the model must be preserved for ongoing litigation, regulatory inquiry, or audit, which dictates retention policies.
• Data Retention Policy Linkage: Reference to the organizational policy dictating the archive's minimum storage duration and eventual secure deletion protocol.
• Access Control List (ACL): Specification of which users or systems have read/write permissions to the archive, ensuring only authorized personnel can retrieve or modify it.

The formal process of decommissioning a model from active production service. This involves:

• Halting all serving traffic to the model endpoint.
• Updating routing logic to direct requests to alternative models.
• Documenting the reason for retirement (e.g., performance degradation, business logic change).
• Initiating the subsequent archival process. Retirement is the action that immediately precedes archival, marking the end of a model's operational life.

A centralized repository for storing, versioning, and managing machine learning model artifacts and metadata. It acts as the system of record for the model lifecycle. For archival, the registry:

• Stores the final, immutable model artifact (weights, architecture).
• Preserves all associated model metadata (training parameters, metrics, data lineage).
• Maintains version history, linking the archived version to its active predecessors.
• Provides search and retrieval capabilities for future audit or reference.

A comprehensive, immutable record tracing the origin, transformations, and dependencies of a model throughout its lifecycle. For archival, lineage provides auditability by documenting:

• The exact training dataset version and preprocessing code used.
• The experiment run, hyperparameters, and environment that produced the model.
• All previous promotions, deployments, and performance evaluations.
• Links to related artifacts like evaluation reports and fairness assessments. This traceability is crucial for regulatory compliance and understanding model behavior post-retirement.

A chronological, immutable log recording all actions, decisions, and changes made to a model and its assets. During archival, the audit trail captures:

• The timestamp and identity of the user/system initiating the archival.
• The business justification or policy trigger for archival.
• A cryptographic hash of the archived artifact to ensure integrity.
• Any access events to the archived model post-retirement. This creates a verifiable chain of custody essential for governance and compliance frameworks like the EU AI Act.

A versioned model artifact that cannot be altered after its creation and archival. Immutability is a foundational principle for trustworthy archival, ensuring:

• The archived model is bit-for-bit identical to the model that was retired from production.
• Reproducibility is guaranteed; the exact model can be reloaded and verified at any future date.
• Integrity is protected via cryptographic hashing (e.g., SHA-256).
• Any "re-activation" requires the creation of a new, distinct version, preserving the original archival record.

A set of rules and standards defining the requirements for model archival to ensure compliance and risk management. A robust policy specifies:

• Retention Periods: How long different classes of models must be archived (e.g., 7 years for financial models).
• Data Sovereignty: Geographic and legal requirements for storage location.
• Access Controls: Strict role-based permissions for retrieving archived models.
• Deletion Protocols: Secure, documented procedures for the eventual destruction of archived assets after the retention period expires.