Menyiapkan Data untuk ML API di Google Cloud: Challenge Lab Ulasan

Mahesh V. · Diulas 3 bulan lalu

Cristopher B. · Diulas 3 bulan lalu

25EU06R0167 - S. · Diulas 3 bulan lalu

Gourab B. · Diulas 3 bulan lalu

DANIEL FELIPE P. · Diulas 3 bulan lalu

Generally good but the requirement for validating the API calls was unclear. I outputted the result directly to a .result file on my filesystem and uploaded that with gsutil, which it didn't accept (although this is a perfectly valid Linux command). It only validated it as correct when I piped the output to a .json file, and then did the rename at the gsutil upload stage. This is poor validation since both ways are correct.

Jacqueline P. · Diulas 3 bulan lalu

Gourab B. · Diulas 3 bulan lalu

Tanuj D. · Diulas 3 bulan lalu

Sachin H. · Diulas 3 bulan lalu

Mateusz B. · Diulas 3 bulan lalu

Rahul K. · Diulas 3 bulan lalu

Gourab B. · Diulas 3 bulan lalu

Aarti D. · Diulas 3 bulan lalu

Mahesh V. · Diulas 3 bulan lalu

Gulshan K. · Diulas 3 bulan lalu

Gourab B. · Diulas 3 bulan lalu

i got problem that my 3rd task not showing completed even after i completed

Nayan V. · Diulas 3 bulan lalu

Davide M. · Diulas 3 bulan lalu

In instructions (dataproc) indication was run the job in a N2 cluster, but impossible to choose that option (only N4 available)

Daniel C. · Diulas 3 bulan lalu

Nice !!!

Zinnoor M. · Diulas 3 bulan lalu

Data allocation for ML API are discretized with parallel and scalable function for a running Dataflow template region enabled in a Cloud Shell region with its compatible Cloud Storage Bucket instance.

Anshuman M. · Diulas 3 bulan lalu

Induja P. · Diulas 3 bulan lalu

Samuel I. · Diulas 3 bulan lalu

MANVI J. · Diulas 3 bulan lalu

Akanksha G. · Diulas 3 bulan lalu