It is good course it contains all required to understand what you need to make and finalize and I learn all steps needed to make model ML app with google. However, there some notes sometimes I miss understand in labs there moving in code fast without explain maybe the labs for us to read later and at the end thanks to share with us your expertise and information

Good overview of end to end ML utilizing GCP starting from preparing the data set from Bigquery , utilizing data lab for building the model on a smaller dataset, Moving to Cloud ML engine to perform distributed training on a larger dataset, using Apache beam for pre-processing the data before serving and google app engine to finally serve the model

The qwiklabs interface to GCP is a little cumbersome. The need to start and stop sessions with each lesson wastes some time. I would prefer if the course came with a GCP credit and we were able to use our own accounts and still have a way to keep track of progress,..

It is an amazing demonstration of what Google Cloud can do in just a few lines of code, but a couple of the labs did not completely work for me, especially when it came to running jobs on Cloud ML. They were not essential, and the experience was still great.

The course is well structured. However, Google moves really fast when creating new products hence there is some confusion when running the labs. That being said, it's amazing that qwiklabs is utilising essentially a live environment for practice.

Needs more explanations about the performance.

I do not understand why processing is so slow.

it is dozens of minutes or even more than 1 hour to process a few gigabytes.

Datalab takes more than 5 minutes to start, why ?

Interesting hands-on course. You can grasp the full workflow from exploring a dataset, select/validate and transform inputs, define the model, train and validate it in a small scale and on Google Cloud Engine.

This course is good to the beginner in first time. But, it has more complexity contents from middle. Also, every labs require quicklabs mission. it's very repeative. I recommend the simple task need to auto.

Overall this is a very well structured and well delivered course i learned a lot from it.

But I couldn't reproduce some of the examples on local machine so 4 stars for now.

The contents are good but some materials have buggy code. (lab 4, lab6, lab7). Those labs cannot finish but I have to accept the concept what the teachers are saying

Pretty good start to the specialization, by reviewing the topics of the previous specialization! Looking forward to the rest of the specialization!

The course is very well managed and very well delivered. The labs gave an opportunity to learn the course from implementation point of view.

Notebooks for labs would not open sometimes. They would just spin and I wouldnt be able to open a new instances (using chrome incognito).

Hits the sweet spot of not trying to teach you model development or TF but still shows how to integrate with the GCP mindset.

This is a very good introduction, I regret not being able to do optional lab 7 (no qwiklabs) an seing the app live

Great overview of the tools needed for deploying models for GCP. 4 stars are only because of lab technical issues.

It's good to quickly overview ML. But some syntax is not so friendly to understand if I didn't see the manual .

Nice content. Would be nice if students are required to write more codes, not just running the written codes .

Very hard to understand all the code, is there any prerequisite for this course? // It is seriously hard.

pretty good for intro to get a feeling of how the Machine Learning System is working as a product.

Great Course with exposure to end to end deployment and Code Sample to learn Faster

More explanation and digging deeper into code would be welcomed

Its good one.

Excelente