Comentários e feedback de alunos de Build, Train, and Deploy ML Pipelines using BERT da instituição deeplearning.ai

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.

G​ives good general overview of Pipelines. However, assignments are way too easy, which makes them not to add too much to the learning.

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

The videos are excellent. The labs are way too easy, just copying some variable names.

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.

Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!

EXcellent MLOps content, presentation, demo

Great Course! Unlimited Thanks to you!

Highly recommended

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 ;-)

Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.

It is difficult to understand completely lab exercises . Very Nice course!!

ok

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.

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.

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.

Detailed code walk through explaining the code would have been helpful similar how it was done in Tensorflow In Practice Specalization

hope there will be code implementation examples in the lectures