the kuberflow lab assignment had technical issues. After submiting the concern did not get any fruitful resolution.

This course is good as an overview. Labs, compared to other courses on this series are lower quality and some are plain broken. It looks like this course is a number of assorted course snippets (from other, older courses) put together into this course.

One of the lab's was broken. It eventually got fixed but it took nearly 2 weeks.

Whilst the content of this course is well structured, what let this course down is the dismal quality of support provided by the Qwiklabs support team.

Many Coursera students undertaking this course were adversely impacted by a technical issue with the lab exercise titled Predict Bike Trip Duration with a Regression Model in BQML. Upon raising a support case to the Qwiklabs support team, it was confirmed that this was a known issue. In spite of that confirmation, no notification was provided to all impacted students to inform them of this and instead, support was poorly handled on a case by case basis.

Impacted students were not provided with any clear, consistent, and detailed messaging by the Qwiklabs support team (e.g. what the issue is, who is taking care of it, when it may be resolved, when another progress update may be available, what is the workaround in the meantime, etc). In addition, aside from an initial response upon raising a support case via email with the Qwiklabs support team, no further responses were provided from them in spite of impacted students following up to request progress updates. There was basically a period of >10 days of radio silence from the Qwiklabs support team.

This is an incredibly disappointing experience and degrades not only the quality of an otherwise great course but also negatively impacts the experience of using the Coursera platform. It is a pity that the instructors of this course who have clearly put in a lot of effort into content creation have been let down by the Qwiklabs support team.

In my opinion this course is very general. It's good for those who want to know what feature google cloud platform offer for machine learning development. The lab was interesting, that we use real world data, but I think it's still lack of in deep explanation. Well, yeah because in this course is more focus for the data engineer not for data scientist or analytic. So 5/5 in my opinion

Flakey - resource issues.

Datastudio issues.

And quiz not registering answers - so not completing properly.

I found the course very helpful for a Data Scientist/Data Engineer to get familiar with the Google Cloud services for Data Analysis and Machine Learning. After this course, I am comfortable at least using AI Notebooks combined with BigQuery. I think I’ll choose Kubeflow as a Service instead of creating a Kubernetes cluster then install Kubeflow. That part and relevant Lab was confusing for me, though this is nothing to do with Google Instructors or Qwiklabs. Basically, Kubeflow, Kubernetes are complex for me still. I recommend the course, take time for the labs.

This course In a very condensed manner teaches about Kubeflow (a Kubernetes based platform for portable and scalable Machine Learning), BigQuery Machine Learning (BQML, a machine learning framework integrated directly into BigQuery data analytics service), AutoML (code-free ML to build advanced ML models on Google infrastructure) and some other remarkable GCP tools/services! The course is amazing and I am surprised that it is not included in the Google ML specializations.

The course is well structured but will need extra exploration of specific subject areas within the course. I hope learners can follow-up with more hands-on experimentation of all the products and services provided at no cost. That is exactly what I plan to do before I take the cloud certification. I do not want to be half-baked neither do I not want to go away with an imposter syndrome.

Really great course with in depth explanations of all important GCP features. It helped me a lot.

Personnally I would prefere not to have pseudo deadlines and a separation of courses into week1/week2 as I did the courses whenever I had time and the separation was slightly distractring. Only a minor point though.

The course was so deep and well organised for easy understanding and also the labs were good too.One thing which was missing is that ,"Solution videos for labs which would be useful for verification".

Thank you for extending my attempts to clear the labs which are difficult sometimes

This suite of courses helped me to get a complete understanding on the delivery of cloud platform in solving distributed computing models. It also gave me complete insight as to how technology liberates the humanity from perennial problems.

An introductory course with good examples of how to use different google tools to develop and deploy machine learning models, very well tough and perfect for a high level understanding of the GCP AI platforms.

its more enough to learn Big Query linking Smart Analytics through ML AI on Google Cloud platform . I am really eager for next level for accomplish Google top certification (GLOBAL CERTIFICATION). Thank you.

Without a doubt the best course, I have learned a lot not only from GCP but from many aspects of cloud computing and the skills necessary for a data engineer. Thank you very much for the opportunity.

Very good ML course to introduce students with Google Cloud machine learning capabilities. Maybe there should be a lab for AutoML (after video lessons), as it exists on Qwiklab platform.

This course helped me to do smart analytics, and in my current job I was able to apply Machine Learning easily on GCP, and I helped my team to the AI platform like experts.

Content was fun and exciting but some exercises/graded labs inside this course are very unclear with the instructions and also took a long time to finish (model training).

Amazing to be part of this great learning Journey!! I am learning concepts and strong fundamentals to build a good foundation.

Excellent course. Gets pretty advanced with developing ML pipelines with Kubernetes Engine, but otherwise very accessible.