Advanced Visualizations with TensorFlow Data Validation Reviews

dailin m. · Reviewed over 2 years ago

Fahad A. · Reviewed over 2 years ago

Pritam B. · Reviewed over 2 years ago

great

Snehal C. · Reviewed over 2 years ago

Hugo M. · Reviewed over 2 years ago

Nirmal R. · Reviewed over 2 years ago

Xiaofeng X. · Reviewed over 2 years ago

Rana A. · Reviewed over 2 years ago

Srikeerti V. · Reviewed over 2 years ago

Omar E. · Reviewed over 2 years ago

Laszlo S. · Reviewed over 2 years ago

This comment in the notebook is not consistent with the "serving data": "We also have an INT value in our trip seconds, where our schema expected a FLOAT. By making us aware of that difference, TFDV helps uncover inconsistencies in the way the data is generated for training and serving. It's very easy to be unaware of problems like that until model performance suffers, sometimes catastrophically. It may or may not be a significant issue, but in any case this should be cause for further investigation. In this case, we can safely convert INT values to FLOATs, so we want to tell TFDV to use our schema to infer the type. Let's do that now." Actually no anomly is detected for "trip seconds" feature.

Giovanna S. · Reviewed over 2 years ago

Abdul Q. · Reviewed over 2 years ago

maziyar a. · Reviewed over 2 years ago

Wagner S. · Reviewed over 2 years ago

Daniel L. · Reviewed over 2 years ago

Suresh M. · Reviewed over 2 years ago

Ritesh S. · Reviewed over 2 years ago

Tannerific V. · Reviewed over 2 years ago

Shubham J. · Reviewed over 2 years ago

Rhon Joshua C. · Reviewed over 2 years ago

Gilles K. · Reviewed over 2 years ago

Ari-Matti V. · Reviewed over 2 years ago

pravalika a. · Reviewed over 2 years ago

Mariam N. · Reviewed over 2 years ago