Metadata and artifact store

As explained in Designing Deep Learning Systems: A guide for software engineers

A centralized system that stores machine learning metadata and related artifacts and connects them for discovery and debugging.

A metadata and artifact store centralizes deep-learning metadata and correlates models, training runs, pipelines, and artifacts, making troubleshooting and discovery practical across teams. section 8.2.2

A useful store enables teams to:

• Search models and training runs through a shared interface. section 8.2.2
• Inspect the parameters, datasets, and training metrics behind a past run. section 8.2.2
• Trace model files back to their training runs to establish model lineage. section 8.2.2

Investigating a Model

A data scientist lists models and training runs in the UI, opens a prior run, and retrieves its inputs, dataset, and metrics without understanding the training or serving infrastructure. section 8.2.2

Figure 8.3 Model troubleshooting with metadata management

Use the troubleshooting architecture to see how the store connects metadata across model development activities. section 8.2.2

How Designing Deep Learning Systems: A guide for software engineers covers it

How deep it goes

Explains collection, correlation, UI retrieval, and lineage discovery at the system level.

Examples

Uses model listings, training-run inspection, linked model files, datasets, parameters, and metrics for evaluation.

What it assumes

Assumes metadata is generated across experiment and production pipelines owned by different teams.

What you can do after

Diagnose/debug with it: retrieve training context and trace a model to its training run.

How it connects

Connects models, training runs, pipelines, artifacts, parameters, datasets, metrics, and model lineage.

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Related concepts

• deep learning systempart of · A software and infrastructure platform that supports the development, training, delivery, and operation of deep learning products or services.
• degradationaddressed by · Degradation is a decline in a system’s or model’s performance, quality, or effectiveness relative to a previous or expected level.

This explainer was generated by Manning's knowledge graph from the text of Designing Deep Learning Systems: A guide for software engineers. The section links point to the parts of the book it draws on.