Nov 18, 2019
awesome learning experience fro the teacher from google. thanks to coursera and google for providing me such a good lesson which will be beneficial for my upcoming future and research work
Mar 03, 2019
Definitely adds a unique perspective on thinking about machine learning systems at scale. This course is suitable for Data Scientists, Data Engineers and Machine Learning Engineers.
por David K•
Mar 12, 2019
Good: Course structure = great, content is relevant and interesting
Bad: Labs do not always work (e.g. deprecated GCP modules incompatible with apache-beam), code for labs already contains answers... would be nice to have "lab" file and "answer" file to make learning more explicit, also, the white guy with the mustache should rerecord his videos.... the cadence is distracting and he does not go into as much depth as Lak
por Ian Q T C•
Jan 19, 2019
Exactly what it says. Labs are trivial and I felt like I didn't learn much other than how to use the interface for serving and taking a model from start to finish. The core concepts are useful both in GCP and if you decide to roll your own stack
Oct 21, 2019
Good to work in a real world environment, though not much hands-on work (most code has already been provided). Still not familiar with API's details.
por Brandon T•
Dec 12, 2019
I appreciate the content being on github but the course had many technical difficulties on GCP. Much of the content in videos was in python2.
por Pablo M P•
Aug 25, 2019
This course is not very practical nor very well explained. The topic is very interesting but it is not delivered clear enough.
Jun 29, 2019
I am satisfied with GCP training except for some errors.
I think I need the latest update.
Nov 02, 2018
the course is helpful for any learner initial to touch GCP learning
por AMAN V•
Dec 15, 2019
The labs could have been a bit more useful.
por Rahul D G•
Jul 13, 2019
QuickLabs has error in many labs
Jun 12, 2019
Jun 30, 2019
por Mark Y•
Jun 22, 2019
por Nikhileshkumar I•
Nov 08, 2019
Course content is good. But Coursera policy is bad. After completion of the course, only a 1-month extension is given to revise it after that. You cant see the content. This is very bad. It would be better to download the videos.
How are you supposed to revise the contents?
After that, you have to pay again. I will think twice before going for any course with coursera.
por Han L•
Nov 23, 2019
Good content, but flawed lab design. The labs are designed to run in sequence and have dependencies from previous labs. Those dependencies break with different sessions of Qwiklabs.
por Nattachai T•
Oct 07, 2019
Most of this course lack the actual coding so I dont think I get much actual experience from this course
por Alireza K•
Jul 27, 2019
The labs were completed, I need to only run them. which I think I couldn't engage myself
Jul 02, 2019
por Jakub B•
Jun 19, 2019
Very weak course. There are no assignments, only 'labs' for which there are walkthrough videos that tell what happens in code, but they don't actually ask to implement or test anything.
The qwiklabs platform is also very unwieldy - even for labs that take 10 minutes you have to go through set up that takes at least 5 minutes...
If you want to take whole specialization the course might be useful, but otherwise DO NOT DO THIS COURSE
por Sacha v W•
Jun 20, 2019
Quite some labs did not work. It shows how several components of GCP can be used. Then there are implementation labs that are really abacadabra. I was really disappointing.
por Russ K•
Aug 30, 2018
Looks like this class could be very useful, but you have to pay up front before you can try any labs. Don't bother auditing.
por charles l•
Dec 13, 2019
Spent more time with Google tech support trouble shooting why their courses didn't work, than I did on machine learning...
por bearrumor T•
Jun 28, 2019
Too short Time and Too Many Contents and Too rare comments for ToDo items in LabTasks
por Grzegorz G•
Feb 12, 2019
Labs are not working. I'm getting 'access to the resource denied' error