Prepare Data for ML APIs on Google Cloud: Challenge Lab Reviews

25EU06R0167 - S. · Reviewed 3 months ago

Gourab B. · Reviewed 3 months ago

DANIEL FELIPE P. · Reviewed 3 months ago

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. · Reviewed 3 months ago

Gourab B. · Reviewed 3 months ago

Tanuj D. · Reviewed 3 months ago

Sachin H. · Reviewed 3 months ago

Mateusz B. · Reviewed 3 months ago

Rahul K. · Reviewed 3 months ago

Gourab B. · Reviewed 3 months ago

Aarti D. · Reviewed 3 months ago

Mahesh V. · Reviewed 3 months ago

Gulshan K. · Reviewed 3 months ago

Gourab B. · Reviewed 3 months ago

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

Nayan V. · Reviewed 3 months ago

Davide M. · Reviewed 3 months ago

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

Daniel C. · Reviewed 3 months ago

Nice !!!

Zinnoor M. · Reviewed 3 months ago

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. · Reviewed 3 months ago

Induja P. · Reviewed 3 months ago

Samuel I. · Reviewed 3 months ago

MANVI J. · Reviewed 3 months ago

Akanksha G. · Reviewed 3 months ago

Vedant N. · Reviewed 3 months ago

25EU10R0079 - V. · Reviewed 3 months ago