ML Metadata Store

Records the directed acyclic graph (DAG) of data, code, and model artifacts that produced a result. For genomic models, this links a specific trained variant caller back to the exact VCF file, reference genome, and training script used.

• Tracks input datasets (FASTQ, BAM, VCF) with content hashes
• Links hyperparameters and code commits to output models
• Enables precise rollback to any point in the experiment history

Captures the complete runtime context of a training job, including the Docker image digest, GPU driver version, and cluster node topology. This is critical for debugging non-deterministic behavior in distributed genomic model training.

• Logs hardware configuration and CUDA/cuDNN versions
• Records execution timestamps and resource utilization metrics
• Ensures computational environment is fully reconstructable

Centralizes the storage and querying of all hyperparameters (learning rate, batch size, optimizer) and evaluation metrics (F1 score, AUROC, perplexity) across thousands of genomic experiments.

• Enables SQL-like querying: 'Find all runs where learning rate > 1e-4 and validation loss < 0.2'
• Supports automated hyperparameter importance analysis
• Prevents metric duplication and ensures consistent logging schemas

Acts as the source of truth that feeds a Model Registry. Once a genomic model passes validation, the metadata store promotes its lineage record to a 'production-ready' stage, linking the binary artifact to its full provenance.

• Manages stage transitions: Staging → Production → Archived
• Attaches deployment annotations and approval gate status
• Prevents unregistered or unvalidated models from being served

Provides an immutable, append-only log of every experiment, satisfying the stringent audit requirements of clinical genomics and pharmaceutical research. An auditor can trace a diagnostic prediction back to the exact training data slice and code version.

• Generates cryptographic hashes for all artifacts
• Supports data provenance queries for regulatory filings
• Demonstrates compliance with GxP and FDA software validation guidelines

Enables diffing between experiment runs to identify the root cause of performance changes. A user can compare two genomic foundation model checkpoints to see that the improvement came from a specific data augmentation step, not a hyperparameter change.

• Visualizes the DAG diff between two experiment executions
• Highlights changes in input data, code, or parameters
• Accelerates iterative model debugging and optimization

A system that statistically compares the distribution of incoming production genomic data against the training data baseline. It relies on the ML Metadata Store to know the reference distribution of the training set. If a model trained on exome data from a European cohort begins receiving whole-genome samples from a diverse population, the monitor triggers an alert. This metadata-driven observability loop is critical for detecting silent model performance decay in clinical genomics pipelines before it impacts patient results.