Model Registry

A model registry provides immutable versioning for every model artifact, similar to Git for code. Each version is uniquely identified (e.g., v1.2.3) and linked to its complete lineage, including:

• The exact training code commit hash
• The versioned dataset used for training
• The hyperparameters and environment configuration
• The evaluation metrics and reports This creates a full audit trail for reproducibility, compliance, and rollback scenarios.

Models progress through predefined stages (e.g., Staging, Production, Archived) with controlled stage transitions. This workflow enforces governance by:

• Requiring specific approval gates (e.g., manual sign-off from a data scientist) before promotion.
• Automating deployment triggers when a model reaches the Production stage, integrating with CI/CD pipelines.
• Providing a clear, auditable history of which model version is active in each environment, preventing configuration drift.

Beyond the model binary, a registry stores rich, searchable metadata and annotations. This includes:

• Technical metadata: Framework (PyTorch, TensorFlow), format (ONNX, SavedModel), size, and required dependencies.
• Business metadata: The model's owner, intended use case, associated business KPI, and relevant documentation links.
• Performance annotations: Key evaluation metrics (accuracy, F1-score, latency benchmarks) from validation datasets. This metadata enables discovery, comparison, and informed decision-making about model deployment.

For Parameter-Efficient Fine-Tuning (PEFT), a model registry must manage two distinct but linked artifacts: the frozen base model and the adapter weights (e.g., LoRA matrices). Key capabilities include:

• Storing the base model as a foundational, versioned asset.
• Versioning adapter modules independently, linking them to their specific base model version.
• Supporting runtime adapter injection, where the serving system dynamically loads the correct adapter. This enables efficient multi-tenant or multi-task serving from a single base model instance.

A model registry is not a silo; it's the central hub integrated with the broader MLOps toolchain. Key integrations include:

• CI/CD Systems: Automatically registers a new model version when a training pipeline succeeds.
• Artifact Stores: References model binaries stored in systems like Amazon S3 or Google Cloud Storage.
• Serving Platforms: Triggers deployments to inference endpoints (e.g., KServe, Seldon Core) upon model promotion.
• Monitoring Tools: Provides model version context to observability dashboards for tracking performance and drift.

Enterprise-grade registries enforce role-based access control (RBAC) to govern the model lifecycle. This ensures:

• Data Scientists can register and test models in development stages.
• MLOps Engineers have permissions to approve promotions and manage deployments.
• Auditors have read-only access to all metadata and lineage for compliance checks.
• Policies can be set to require specific documentation or evaluation thresholds before a model can be marked for production, enforcing quality gates.

An artifact store is a versioned storage system (e.g., S3, Google Cloud Storage) for the raw outputs of machine learning pipelines. It is the foundational storage layer for a model registry.

• Stores serialized model binaries, training datasets, evaluation reports, and preprocessing artifacts.
• Provides immutable, versioned storage with unique identifiers.
• The model registry adds a management layer on top, tracking lineage, metadata, and stage transitions for the artifacts stored here.

Model versioning is the core practice of uniquely identifying and tracking different iterations of a machine learning model and its dependencies. It is the primary function enabled by a model registry.

• Assigns unique identifiers (e.g., v1.2.5) to each model iteration.
• Tracks the code commit, training data snapshot, hyperparameters, and environment used to create each version.
• Enables reproducibility, audit trails, and safe rollback to previous versions.

CI/CD for ML (Continuous Integration and Continuous Delivery/Deployment) extends software engineering practices to automate the testing, building, and deployment of ML models. The model registry is the central hub in this pipeline.

• Continuous Integration automatically tests model code and data.
• Continuous Delivery packages the trained model and registers it as a new version in the registry.
• Continuous Deployment automatically promotes registered models through staging to production based on validation gates.

Model serving is the infrastructure and process of deploying a trained model to make predictions available via an API. The model registry provides the approved, versioned model artifact for serving systems to deploy.

• Serving platforms (e.g., Triton, KServe, Sagemaker) pull specific model versions from the registry.
• The registry dictates which model version is currently in staging or production.
• Enforces governance by ensuring only registered, validated models are deployed.

ML pipeline orchestration is the automated coordination of sequential tasks in an ML workflow (data, train, evaluate). The orchestrator triggers the registration of successful pipeline outputs.

• Orchestrators like Apache Airflow, Kubeflow Pipelines, or MLflow Pipelines execute the workflow DAG.
• The final task of a training pipeline is typically "Register Model," which pushes the new model version and its metadata to the registry.
• Creates a direct link between pipeline runs and registered model versions.

Adapter versioning is the practice of applying model versioning principles specifically to Parameter-Efficient Fine-Tuning (PEFT) adapter modules. It is a critical specialization for PEFT deployment.

• Tracks lightweight LoRA or Adapter weights separately from the frozen base model.
• The registry stores the mapping between a base model version and its associated adapter versions.
• Enables multi-adapter inference, where a single base model can dynamically load different registered adapters for multi-task or multi-tenant serving.