Not enough hands-on labs. Mostly just videos and running existing python code. Had to write only a tiny amount of code. I won't remember any of the code, I'll just remember ctrl-enter on all of the pre-written python.... could definitely use some improvement.

Quite outdated this course needs to be updated or removed.

This specialization consists of 5 courses:

Course1: End-to-End Machine Learning with TensorFlow on GCP

Course2: Production Machine Learning Systems

Course3: Image Understanding with TensorFlow on GCP

Course4: Sequence Models for Time Series and Natural Language Processing

Course5: Recommendation Systems with TensorFlow on GCP

In specialization's FAQ say nothing about "audit" option. Do You know what is it ? "Audit" means that You can use course video material even after You subscriptions ended.

By fact, only "Course 1" has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization:

courses 2-5 has special point:

"Why can’t I audit this course?

This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

"

"Who have paid" means that after You subscriptions ended, you lost access to video materials in this courses.

p.s.

1 star only for "Audit", content and lecturers are rated higher - at least 4 stars

This course was created in end of 2019. It will be much better to update the lab materials to Tensorflow 2.0 and use Keras abstraction. The goal is to learn how to deploy without going into too much details. Some of the code doesn't work in 2.0 anymore.

Spent more time with Google tech support trouble shooting why their courses didn't work, than I did on machine learning...

Would be nicer if students can use the google cloud with less restrictions. I got blocked multiple times from trying the codes in the videos..

Overall, the materials are great and very interesting!

It needs to be updated, as soon as possible.

Objective of course is great. But, a lot can be improved in terms of clarity on how to execute this course. It is not clearly mentioned whether we need to just execute provided notebooks or write code from scratch. Moreover, how to copy these notebooks from Google cloud repo on github is not mentioned anywhere.

I appreciate the content being on github but the course had many technical difficulties on GCP. Much of the content in videos was in python2.

This is very dated. I presume there is a newer version and would be good if you pointed people to the new version

The course walks you through a few common tools of Google AI Platform. The content is a little bit shallow (~ 5-6 hours work) and not well prepared. In the lab, the only thing that you need do it just click the "run" button, the results are not really verified. After taking the course, you will have a basic knowledge of what tool to use for the common ML development tasks, however to start a real project, there are many things to be learned by yourself and is not being covered by this course.

Not worth of investing Time on it.

One of my favourites Courses from Google, it provides an excellent overview of the End to End Machine LEarning process, and how to use GCP to speed up your workflow, highly recommended to anyone looking to expand their understanding of Real World application of ML

The course content is rich and easy to understand. The instructors are also awesome and the quick labs are very helpful to understand the concepts in the practical world.

Really, really poor due to the fact that it's outdated to the point being broken in various ways. For instance, Qwiklabs confirms there is a bug in the local prototyping lab that prevents us from actually training anything. There is no consensus between video and lab as to whether we are using Keras or Estimators. Lab 5 points to an old ipynb using Estimators, while Lab 6 assumes we have used a newer version featuring Keras--so one redoes all of Lab 5. There is a "Optional Lab" with accompanying videos, but where there is no option to do the lab--the lab has been removed for half a year or more (per the discussion board). The video summary mentions that we have used App Engine to make a frontend for our model--no we didn't! I understand having a few bugs, but this course surely wasted several hours of my time. And yes, as others mention, the TODO's, for the most part, don't require much thought. On the upside, I think Lak is a really excellent presenter, and that Chris and the other guy are also pretty good.

The labs were out of sync with the reviews and the code was either 100% complete or 0% complete, there wasn't much of an opportunity to learn.

Across the 1 course they used 2 different notebook applications as it was updated, which was confusing. They also scattered Tensorflow / Tensorflow.Keras throughout and it lacked consistency.

Also the demonstrators shouldn't speak into the mic so closely.

Some content is out-of-date and the labs are mostly solved with no much guidance. I was expecting a better explanation on what are the key points in the labs, such as the commands needed to create training jobs and so on.

Good course overall, maybe the labs are too short or too easy, maybe it would be better to have a link to a doc with some related tasks.

Two labs were missing, one I could see while looking around in the notebooks, but the last one I didn't, i would have appreciated since I don't know flask yet

This first course of the specialization allows to jump from the ML model that one can play in its own computer, to the model that you can scale to become business operative.

It is not only about build the model, it is about publish the model to make it avalible to your customers or consumers!

One of the best course in the series! A comprehensive tutorial that walk your through the whole end-to-end process of machine learning. Hope there is more similar courses like these where we can get our hands dirty with practical end-to-end machine learning case studies!

I genuinely thank the course co ordinatos for such an amazing experience. The amount of information i was able to adsorb was just fabulous. Once again, a worthy course to go for if you're keen in learning machine learning end to end process. Thanks alot!

This course contains many helpful hands-on labs. The course instructors are very knowledgeable. I would recommend this course to anyone who is interested in learning about production machine learning pipeline using TensorFlow.

This course is very help full for the beginners where one can learn the ML model to Create , Train , Evaluate & Deploy , very live examples and hands on lab .

My sincere thanks to the training instructors for their efforts.

Very practical course. I recommend it to people who already has good ML background and know how ML algorithms work. It is a great practical introduction on how to quickly build and deploy your model in GCP

I would like to thank Lak and Chris for their wonderful presentation of the deployment of ML models on the Google Cloud Platform. The case study problem chosen for the course is also unique.