The content is decent. But the labs are pretty broken, not well designed & maintained

Qwiklabs does not work!!

There's a few things underwhelming about this course. First, GCP has made MLops very complicated, technical and cumbersome. Since you would need to work with this tech on a regular basis, you really don't want this. Second, the tutorials are mostly challenging due to linux. The tutorials are also buggy and setting up the cloud resources takes a lot of time. Overall, not that happy with this course or the subject mater.

The qwiklabs have many issues, and due to the limited amount of tries I was not able to complete the course.

By far the worst experience. Videos and explanations are really good but all those goodness are killed by the Qwiklabs experience. Labs are frustrating because they don't simply work, not because you did something wrong. I would like to urge the team behind this course to put some effort and time fixing those labs and answer to the questions raised by the learners in discussion forums. By copy pasting the readymade answer to email qwiklabs support team won't help at all.

The Labs could be improved (bugs and clarity)

The content related to MLOps on GCP is quite good. If the labs were improved slightly to remove some of the bugs that are commonly posted in the message boards, this would be a 5 star.

Course content was good. However, many of the Qwiklabs had bugs, resulting in not being able to complete the course with a grade of 100%.

Accent is difficult to understand. Speaks to quickly. Cannot read subtitles and course content at the same time.

Some labs are impossible to complete due to incompatibility with github. Github requires verification email.

This was a good course along with google qwiklab which guide you through out the lab which makes a enrolled person a successful learner .

Great course to start for learning about MLOps. However, I hope there will have more videos to explain details on LABs.

I liked it. Made me realize how much of a pain MLOps really is.

Finish the course , and need to pay for the certificate .Bad Way for Coursera like goes from EDX .

A neverending stream of jargon and self-promotion with occasional learning

quiklabs always have a trouble when I try this cource..

VERY HELPFUL AND KNOWLEDGE BASED COURSE. THANKS TO ALL THE INTRUCTORS.

Some Labs isn't working properly

while this course teaches some useful skills, in particular how to to offload ML workloads to GCP, and introduces Kubeflow well, it doesn't go into enough depth to really let the students master the material. It doesn't help that Kubeflow (and its GCP implementation) are fundamentally fairly complicated technologies that compete with other, more mature (but less specialized) tools like Airflow. All in all, a good starting point, but don't expect to master the material - further study will be required. This course only scratches the surface.

Videos are nice and good for learning new perspectives but there is a huge problem in this course. Labs (required to complete if you want certificate) are bugy and for example i need to wait for one lab problem to be solved if i want my certificate (which is going on for more than 2 weeks as i can see in forums). Overall, good quality videos but unexpectedly very poor technical management.

Not able to complete. Error: RuntimeError: Training failed with: code: 8 message: "The following quota metrics exceed quota limits: aiplatform.googleapis.com/custom_model_training_c2_cpus"

Some labs are "unavaibale" in the Quicklabs portal. For example, in one Lab the portal says "Sorry, Using custom containers with AI Platform Training is currently unavailable"

The last lab is no longer working.

This is one of the best course to start on ML OPS with GCP. The Concepts were explained neatly throughout the course, and i am sure this would really help me to solve the most complex use cases in deploying ML Models. Thanks Google for this wonderful course and many appreciations to Qwiklabs for hands-on. Highly recommended for ML Engineers/ Data Scientist.

The whole process of building the Kubeflow pipelines for MLOPs including the configuration part (what does into the Dockerfile, cloud build) has been explained fully.