tensorflow/model-analysis

Health v2 · maintenance only

Adoption not part of the score

1265 stars · 279 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow Model Analysis (TFMA) is a Python library for evaluating TensorFlow models on large datasets in a distributed manner. It computes trainer-defined metrics over different data slices and visualizes results, e.g., in Jupyter notebooks.

Use cases

• evaluate tensorflow models on large datasets
• compute model metrics over data slices
• visualize model evaluation results in jupyter notebooks
• compare model performance across subgroups
• detect model bias with fairness metrics
• integrate model evaluation into tfx pipelines

When to choose

• you train models with TensorFlow and want consistent metrics between training and evaluation
• you need distributed evaluation over large datasets
• you want slice-based analysis or fairness metrics for model debugging

When to avoid

• your models are built with PyTorch, JAX, or scikit-learn rather than TensorFlow
• you only need simple evaluation on small datasets where a single-machine library suffices
• you need a stable 1.0 API since TFMA may introduce breaking changes before 1.0

Facets

library · maturity active

machine-learning data-visualization analytics benchmarking machine-learning data-science analytics developer-tools python cross-platform tensorflow model-evaluation tfma tfx slicing-metrics jupyter fairness-indicators

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