model-analysis
A library for evaluating TensorFlow models on large datasets with distributed computation and slicing analysis.
What is model-analysis?
TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models on large datasets using distributed computation. It enables users to compute training metrics over entire datasets and specific slices of data, helping to identify performance issues and biases. The results can be visualized interactively in Jupyter notebooks.
Machine learning engineers and data scientists working with TensorFlow who need to evaluate model performance at scale and analyze metrics across different data segments.
Developers choose TFMA for its seamless integration with TensorFlow, scalable distributed evaluation via Apache Beam, and powerful slicing capabilities that provide deep insights into model behavior across diverse data subsets.
Overview
Model analysis tools for TensorFlow
Use Cases
Best For
- Evaluating TensorFlow models on large-scale datasets
- Analyzing model performance across different demographic slices
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
