The Good: The instructors were clear and concise and provided just the right amount of context and justification for the concepts they presented. The Bad: The labs were very burdensome, unstable, and provided very little toward the learning for the course. Even worse: The Qwiklabs experience, having to interact with their support multiple times, was unbelievably frustrating; Bad enough in fact that I doubt I would ever take another Coursera course if it is coupled with Qwiklabs. Very unfortunate.

Quicklabs support is poor. Labs fail to implement (labs in weeks 2 and 3). Instructions are not clear for some labs. This is the las module in this specialization. It is pity that the last module wasnt implemented well and spoils the whole impression for the entire course.

The labs are broken and you cannot complete this certificate following instructions.

And if you don't follow instructions you can get banned from the labs.

Join this course at your own risk.

After going that far in this specialization, the lab week 2 asking to bind a trigger to github was the end of my journey.

The course content is advanced and great for theoretical learning. The practice labs and support by Qwiklabs make it a 2 star course as the

1) content of the labs are not updated to the latest vertex AI platform and outdated

2) the python virtual environment package dependencies are insufficient

3)the Qwiklabs instances that are provided are not powerful enough to complete labs in the given time.

4) Support from qwiklabs point to the same lab instructions but no additional inputs to fix the issues

Also, the issues mentioned above have been outstanding for over 6 months and unfortunately this course is not updated and is a blocker to earn the Coursera GCP ML Professional Certificate.

I have tried each of the labs 2-3 times but could not complete them due to above

On the positives, this is good course for advanced practices and capabilities of ML pipelines on GCP. Also, this is the only course that I had challenges finishing labs and I completed labs of 8 of 9 courses without issues seamlessly.

Thanks Coursera and I hope Qwiklabs revises the content

Solutions are not provided in lab in correct manner. thers is no solution availabe on internet .

The course is fine but guided labs little out of sync for the content.

Labs are poorly written

Lots of issues

qwiklabs provide only standard solution which doesn't resolve any issue

Averagae turnaround time of qwiklabs for tickets are pretty high (more than a week) for any bugs in the lab

They are pretty fast in the quota related issues

Expected some reliability for google courses!

Labs are so full of bugs and instructions are not at all clear.

Very complete course that dive deep into the functioning of TFX pipelines, orchestrations, CI/CD, showing tools and resources we can use to automate the maintence of ML process. Thank you all for this.

This is a great course to learn how to apply MLOps principles in large scale machine learning projects. I'll refer to this course in the near future to bring its concepts to customer ML platforms.

very nice and easy to undertand concepts , hope for more new such free contents , thanks to google , quicklab , coursera for providing this opportunities .

Very good, thanks!