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MLOps fundamentals is a good introduction, great teachers! The only place that I feel needs improvement is the lab - it would be great if there is more time to do the exercises, the lab gets timed out at 2 hrs. Sometimes the lab instruction are not very clear. Also I would be happier If the instructors went through other build tools like Bazel, etc.... This course helped organize ML workflows and make it easier to experiment, deploy and iterate over model dev.... Overall a very good course!!

github repo used throughout the code will probably serve as a good template for my future projects. The quizzes are on the easy end. The labs can be achieved by a series of copy+paste. Some give the full points for just opening the notebooks without even running them (same set of steps in two of the labs that only differ in notebook content). Feels like I have a lot to go before I'll be able to use these tools for my own tasks. Nevertheless - got to start somewhere.

Enjoyed the course and it is very interesting. Although there is no formal "prerequisite" for the course, you will get much more if you have various basic concepts in AI/ML, python, Jupyter notebook, CI/CD & Google Cloud Build, K8S & GKE, YAML, Github - especially for the labs, I really enjoy them. You might see some people saying that they hit minor problems - in fact, those minor problems are also part of the learning.

It was amazing course and content. No doubt that you are best content provider for the study material. you are feeling the gap between industry and university. As a learner i also faced some difficulty which you need to review it once in "QUICKLABS" cluster creation.

THANKS :)

Ronit Sagar

I learnt new concepts in machine learning through google cloud platform and i am so happy for that. Thank you Coursera for giving this opportunity to gain Google certification and i learnt a lot about google cloud, Kubeflow, and had practical experience through graded external tool.

Very good way to get updated on all the MLOps stacks by GCP. However, the information is super compressed and there are many topics that one has to cover.

Some basic DevOps topics will also be taught during the first week, so you can try to partake even if you have no DevOps knowledge.

The overview of ML Ops as a discipline is very very good, and contrasts with Dev Ops and Software Engineering as co-disciplines. The lab is fairly basic but a decent introduction, however little time is spent explaining its application to ML Ops.

Good course for overall view of Kubeflow orchestration, basics of kubeflow and containerisations and ML ops services available on GCP. Highly recommended if you wanna deploy models with best practices!

Loved the content, labs, and regularly intervened quiz. The only suggestion is that, for Juniper Labs, a detailed video solution would have added more value to this course.

I think there should be more content about AIML can be better choice or preferable.

Otherwise all the things are okay I enjoyed this course and learn a lot.

ThankYou So much.

It is a good designed course, but I would prefer to have basic knowledge of Machine learning and data science in order to understand this course even much better.

A few of the labs didn't work, had to contact support. Also would be good to point to documentation for various tools like kfp-cli

Otherwise good.

Content well written. Some lab issues. Resolved but frustrating. Language processing a bit off on transcribed material from speakers.

The course is quite educational, yet the lab material can sometimes be confusing, especially for beginner users

It's ok. There are example notebooks to understand the code. The pricing part is missing.

Good starter on basic MLOps on GCP for those who want a quick dive and a hands on project

It was good experience learning about the deployment process of ML application on GCP.

I miss a chapter on automating monitoring models when metrics diverge