TensorFlow Data Validation Course
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TensorFlow Data Validation Overview
What is TensorFlow Data Validation?
TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX). TF Data Validation includes:
- Scalable calculation of summary statistics of training and test data.
- Integration with a viewer for data distributions and statistics, as well as faceted comparison of pairs of features (Facets)
- Automated data-schema generation to describe expectations about data like required values, ranges, and vocabularies
- A schema viewer to help you inspect the schema.
- Anomaly detection to identify anomalies, such as missing features, out-of-range values, or wrong feature types, to name a few.
- An anomalies viewer so that you can see what features have anomalies and learn more in order to correct them. For instructions on using TFDV, see the get started guide and try out the example notebook. Some of the techniques implemented in TFDV are described in a Caution: TFDV may be backwards incompatible before version 1.0.
Course Category: Machine Learning
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