Subscribing to this course only gives you option to run assignments on Qwik labs, and they're very poor for these kinds of assignments. You won't get any feedback on assignments anyway since there is no grader.

If you want to check out the material it's better to just clone training-data-analyst from github and do these assignments on GCP free tier.

A lot of great production examples, labs and reviews but perhaps too many issues for a single course - however I understand that it was perhaps to provide an overview of the possibilities, a kind of "toolbox" for production ML.

It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations

I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative

It was bit hard course but lab work was great and learn many production level consideration for ml systems.

Very practical which was nice. Thank you for adding the Quicklabs that helped a lot.

While most of the content is sufficiently informative for a course, the implementation itself has too many issues: wrong videos in some modules, errors in quizzes, and so on. Once they organize the material properly, this course can definitely be 5 stars.

The first module was really good, but the others just seemed like an ad for GCS. Also, the 3rd and 4th module the labs / lab video was hard to follow and felt like I was just reading random code.

Too short and fast. For people who are not acquainted with cloud platform and other google tools, it would be difficult to understand. Aslo the tutorial videos are also not descriptive.

Very theoretical.

Overall this is a very good course.

+ Working as a less experienced data scientist, I gained a lot of hands-on knowledge when putting a machine learning model into production. Especially the dos and don'ts as well as what to pay attention to.

- It would be better to provide more architectural overviews, or further readings, regarding ML on Google Cloud, just for people with less GCP knowledge to catch up easily. Also 2 hour for an exercise is a bit short as I need to hurry up when there is a lot to do in that exercise. Even though I can also start the same exercise 2nd time, it is a lot of hassle to repeat the previous steps. I would suggest that depending on the % of people which can finish the lab, extend the hours for some of the users. After all you have this data and can do some analysis, isn't it? ;)

I really enjoyed the course. It gives the insights on things that are to be taken into consideration while making ML models in production. Though the course focuses on GCP, the learning can be easily applied to other platforms.

This course, I believe, will be crucial to my future understanding of end to end ML applications. I only hope gain more practice in assembling all the pieces from experimentation, to model design, to data engineering.

This course reveals some practical techniques in Production Machine Learning Systems. I like the real world examples introduced in this course.

Rich course, although a little tedious, the info is priceless almost all the time. good for consultation

I walk through the whole system for the entire process of ML so that I could get insights on the forest

very good for people who will enter the production stage on machine learning systems

This is incredible material and incredible technology; I'm a changed person now!

very good information. Lot of unknown facts in ML are brought up in the course.

This was a great opportunity to learn the Production Machin learning system.

direct to the point practical guidance for dev stage prod sequence

I got lots of new skills and I think it's a great course for ML

Really focuses on topics for building production ML Systems

Awesome course for Production of machine learning mode.

Excellent course on architecture for ML systems