ML Metadata
lastmod 2026-02-04created 2026-02-041 edits
ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.
NOTE: ML Metadata may be backwards incompatible before version 1.0.
About
ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows. MLMD is an integral part of TensorFlow Extended (TFX), but is designed so that it can be used independently.
Every run of a production ML pipeline generates metadata containing information about the various pipeline components, their executions (e.g. training runs), and resulting artifacts (e.g. trained models). In the event of unexpected pipeline behavior or errors, this metadata can be leveraged to analyze the lineage of pipeline components and debug issues. Think of this metadata as the equivalent of logging in software development.
MLMD helps you understand and analyze all the interconnected parts of your ML pipeline instead of analyzing them in isolation, and can help you answer questions about your ML pipeline such as:
- Which dataset did the model train on?
- What were the hyperparameters used to train the model?
- Which pipeline run created the model?
- Which training run led to this model?
- Which version of TensorFlow created this model?
- When was the failed model pushed?