Visualizações avançadas com o TensorFlow Data Validation avaliações

Md B. · Revisado há over 2 years

Muhammad I. · Revisado há over 2 years

Siddhesh N. · Revisado há over 2 years

Dave S. · Revisado há over 2 years

kishore k. · Revisado há over 2 years

done

Kishore K. · Revisado há over 2 years

Rupesh P. · Revisado há over 2 years

julien P. · Revisado há over 2 years

Aljon P. · Revisado há over 2 years

Awesome!

Luis Ángel M. · Revisado há over 2 years

Manuel P. · Revisado há over 2 years

I had to complete the lab locally due to the course being out of date

Matthew V. · Revisado há over 2 years

dailin m. · Revisado há over 2 years

Fahad A. · Revisado há over 2 years

Pritam B. · Revisado há over 2 years

great

Snehal C. · Revisado há over 2 years

Hugo M. · Revisado há over 2 years

Nirmal R. · Revisado há over 2 years

Xiaofeng X. · Revisado há over 2 years

Rana A. · Revisado há over 2 years

Srikeerti V. · Revisado há over 2 years

Omar E. · Revisado há over 2 years

Laszlo S. · Revisado há over 2 years

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. · Revisado há over 2 years