Artifact Storage

A model registry is a centralized repository for storing, versioning, annotating, and managing the lifecycle stages (e.g., staging, production, archived) of trained machine learning models. It acts as a source of truth for production-ready models, linking them back to the specific experiment runs and artifacts that generated them.

• Key Functions: Version control for model binaries, metadata storage (author, metrics), lifecycle stage transitions (development → staging → production), and deployment orchestration.
• Integration with Artifacts: A registry typically references a model file (e.g., a .pkl or .pt file) stored in an artifact repository, along with its associated evaluation reports and dependency snapshots.

DVC (Data Version Control) is an open-source version control system for machine learning projects. It manages large datasets, models, and experiment artifacts alongside code using Git, while storing the actual large files in remote, cost-effective storage (S3, GCS, etc.).

• How it Works: DVC replaces large files in your Git repository with small, human-readable .dvc metafiles that act as pointers. The actual file content is stored in a configured artifact storage backend.
• Artifact Management: It provides commands to track, version, and share datasets and model files, making the entire project pipeline—code, data, and models—reproducible. It is often used in conjunction with experiment tracking tools to ensure full lineage.

Lineage tracking, or data provenance, is the practice of recording the complete origin, transformations, and dependencies of data, code, and models throughout the machine learning lifecycle. It answers the question: "What exact data, code, and parameters produced this specific model artifact?"

• Core Purpose: Ensures auditability, debugging, and regulatory compliance by creating a directed acyclic graph (DAG) of all pipeline steps.
• Connection to Artifacts: Every artifact (model, dataset, metric plot) is a node in this graph. Effective lineage tracking requires immutable, versioned artifact storage to guarantee that the referenced files never change, preserving the integrity of the provenance record.

Model checkpointing is the practice of periodically saving the full state of a training run—including model weights, optimizer state, and epoch number—to disk during the training process. These checkpoints are a primary type of artifact.

• Primary Use Cases: Enabling recovery from hardware failures, implementing early stopping, and allowing evaluation of intermediate model versions.
• Storage Implications: Checkpointing generates large, frequent writes to storage. A robust artifact storage system must handle this volume efficiently and provide fast read access for resuming training or performing run comparisons. Checkpoints are often the largest artifacts in a training run.

A pipeline run is a single execution instance of a multi-step machine learning workflow (e.g., data prep → training → evaluation). Each step's inputs, outputs, code, and parameters are tracked to establish full lineage.

• Artifacts as Outputs: The outputs of each pipeline step (processed datasets, trained models, evaluation reports) are persisted as versioned artifacts. The pipeline run metadata links these artifacts together, creating a reproducible chain.
• Orchestration: Tools like Apache Airflow, Kubeflow Pipelines, or Metaflow orchestrate these runs, relying on centralized artifact storage to pass data between steps and archive final outputs.

An environment snapshot is a complete record of the software dependencies, library versions, and system settings used during a machine learning run. It is a critical artifact for recreating the exact runtime conditions.

• Common Formats: Files like requirements.txt (pip), environment.yml (Conda), or Docker images.
• Storage and Reproducibility: While often small text files, these snapshots are stored as artifacts alongside model binaries and metrics. To truly reproduce a run, one must be able to retrieve the exact model file and the environment snapshot used to create it. This prevents "it works on my machine" problems in collaborative settings.