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.
Oct 07, 2018
A very helpful course, we were able to practically apply all the knowledge we received from the First Specialization. I feel much more confident to do ML after this course!
Jun 12, 2019
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.
創建者 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
創建者 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
創建者 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.
創建者 Grzegorz G•
Feb 12, 2019
Labs are not working. I'm getting 'access to the resource denied' error
創建者 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.
創建者 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
創建者 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.
創建者 bearrumor T•
Jun 28, 2019
Too short Time and Too Many Contents and Too rare comments for ToDo items in LabTasks