SL
Jul 5, 2021
It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks
YV
Jul 27, 2021
Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!
By Pablo A B
•Jul 5, 2021
Gives good general overview of Pipelines. However, assignments are way too easy, which makes them not to add too much to the learning.
By Mark P
•Sep 13, 2021
Coding exercises are a bit too structured, there isn't as much learning as I would have liked. That said, having the notebooks for reference at work is quite useful. Good introduction.
By Sneha L
•Jul 6, 2021
It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks
By Israel T
•Jun 19, 2021
Great for introduction to the AWS Sagemaker tools. But if you really want to dive deeper on the tools, you need to add and explore other resources, since most of the codes are already provided in the exercise.
By Magnus M
•Jun 14, 2021
The videos are excellent. The labs are way too easy, just copying some variable names.
By Sanjay C
•Jan 17, 2022
I was a little disappointed in the courses in this specialization - the issue is that a large part of the coding was already done. In order for this course to be an "advanced" level course, the students should be asked to write their own SQL/pandas/python code for database access and data processing.
By Parag K
•Oct 22, 2021
Detailed code walk through explaining the code would have been helpful similar how it was done in Tensorflow In Practice Specalization
By Md. W A
•Mar 27, 2022
Unable to complete Practical Data Science Specialization because grading system does not work.
By Aleksa B
•Nov 2, 2021
Very good course. Highly recommended.
One thing that I would add is to go more in depth about certain concepts (like pipelines) and go through a bit more complex examples in practical exercises.
Overall good job, love it, thank you.
By yugesh v
•Jul 28, 2021
Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!
By RLee
•Jul 28, 2022
Very hands-on AWS BERT labs! Expecting more labs coming...
By Janzaib M
•Apr 17, 2022
Very Hands On Practical Information for the Industry
By The M
•Apr 24, 2022
Exactly the material I am looking for. Fabulous.
By Ozma M
•Jul 18, 2021
EXcellent MLOps content, presentation, demo
By Anzor G
•Dec 27, 2021
Great Course! Unlimited Thanks to you!
By Tenzin T
•Sep 7, 2021
Highly recommended
By John S
•Oct 6, 2021
This is NOT a course about BERT, it's a course about Amazon SageMaker ML Ops. I learned plenty of useful stuff about Amazon SageMaker, I learned nothing new about BERT. The content is a mixed bag - week 1 is poor quality, week 2 is good quality, week 3 is very good quality. The labs aren't great - trivial "fill-in the missing variable/term" style (which, ironically, can probably be done automatically by a BERT model nowadays ;-)
By 妿´² 刘
•Feb 6, 2022
As a machine learning engineer i never met automl in my career before. This course shows me the power of automl. But the lab2 need too mucn time to traing the model, i hope the providers could add 2 hours in that assignment lab.
By Alexander M
•Jul 22, 2021
Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.
By Diego M
•Nov 20, 2021
It is difficult to understand completely lab exercises . Very Nice course!!
By Burhanudin B
•Jun 3, 2022
This is amazing course
By Mosleh M
•Aug 6, 2021
ok
By Muneeb V
•Dec 14, 2021
The lectures video are good but there are some issues with labs. It was taking time to load and the allotted time was less than the required time for the lab. Moreover, there were access denied issues in the lab code.
By Clashing P
•Oct 8, 2021
hope there will be code implementation examples in the lectures
By Vitalii S
•Mar 20, 2023
Mistakes, mistakes, mistakes. As if the author of these works is not AWS but me. Get ready, out of 2 hours of work, it will take 15 minutes to work. And 1.5 hours to correct errors.