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Learner Reviews & Feedback for ML Pipelines on Google Cloud by Google Cloud

3.5
stars
71 ratings

About the Course

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...

Top reviews

MH

Mar 6, 2021

This is a great course to learn how to apply MLOps principles in large scale machine learning projects. I'll refer to this course in the near future to bring its concepts to customer ML platforms.

BK

Sep 24, 2022

very nice and easy to undertand concepts , hope for more new such free contents , thanks to google , quicklab , coursera for providing this opportunities .

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1 - 19 of 19 Reviews for ML Pipelines on Google Cloud

By Daniel L

•

Apr 11, 2021

The Good: The instructors were clear and concise and provided just the right amount of context and justification for the concepts they presented. The Bad: The labs were very burdensome, unstable, and provided very little toward the learning for the course. Even worse: The Qwiklabs experience, having to interact with their support multiple times, was unbelievably frustrating; Bad enough in fact that I doubt I would ever take another Coursera course if it is coupled with Qwiklabs. Very unfortunate.

By Javier J

•

Oct 5, 2021

The labs are broken and you cannot complete this certificate following instructions.

And if you don't follow instructions you can get banned from the labs.

Join this course at your own risk.

By GK

•

Nov 2, 2021

Quicklabs support is poor. Labs fail to implement (labs in weeks 2 and 3). Instructions are not clear for some labs. This is the las module in this specialization. It is pity that the last module wasnt implemented well and spoils the whole impression for the entire course.

By Pierre-Yves D

•

Dec 4, 2021

After going that far in this specialization, the lab week 2 asking to bind a trigger to github was the end of my journey.

By Chaitanya K

•

Sep 3, 2022

The course content is advanced and great for theoretical learning. The practice labs and support by Qwiklabs make it a 2 star course as the

1) content of the labs are not updated to the latest vertex AI platform and outdated

2) the python virtual environment package dependencies are insufficient

3)the Qwiklabs instances that are provided are not powerful enough to complete labs in the given time.

4) Support from qwiklabs point to the same lab instructions but no additional inputs to fix the issues

Also, the issues mentioned above have been outstanding for over 6 months and unfortunately this course is not updated and is a blocker to earn the Coursera GCP ML Professional Certificate.

I have tried each of the labs 2-3 times but could not complete them due to above

On the positives, this is good course for advanced practices and capabilities of ML pipelines on GCP. Also, this is the only course that I had challenges finishing labs and I completed labs of 8 of 9 courses without issues seamlessly.

Thanks Coursera and I hope Qwiklabs revises the content

By Parth S

•

Aug 19, 2022

Solutions are not provided in lab in correct manner. thers is no solution availabe on internet .

By Bincy B N

•

Dec 19, 2022

Labs are poorly written

Lots of issues

qwiklabs provide only standard solution which doesn't resolve any issue

Averagae turnaround time of qwiklabs for tickets are pretty high (more than a week) for any bugs in the lab

They are pretty fast in the quota related issues

Expected some reliability for google courses!

By Steven S

•

Dec 19, 2022

Labs are so full of bugs and instructions are not at all clear.

By Kurapati V S M K

•

Nov 30, 2021

The course is fine but guided labs little out of sync for the content.

By Ramesh Y

•

May 28, 2023

Content was more like monologue, having proper video/animations would certainly help to new users who are new to these concepts. I felt like some monologues are just going on and people are simply reading their contents and assume people are pro on the other side. I would have loved some interactive video explaining concepts or any animation video where I can see things are working

By Benjamin C

•

Dec 14, 2023

poorly designed labs require self writing lab code that is not intended.

By Tomas V d B

•

Jan 24, 2024

dysfunctional course

By Stanislav B

•

Mar 7, 2024

- Pros: interesting topics were touched - Cons: 1. Presenters are "talking-heads" reading documentation-like script 2. Slides are detached from what presenter is saying and rather distract because one have double cognitive load: follow monotonic presenters talk and align it with "managerial Comics" shown on the slide 30% of a time. Other 30% are random code pieces, rest 60% is something truly useful. 3. Labs aren't possible to finish by following instructions. Mostly because they are outdated, which causes dependency hell and errors here and there. Even by googling, applying hot-fixes and hacking the packages I wasn't able to get 100% of them done.

By Max C

•

Mar 4, 2024

Half the assignments don't work. Staff do not even reply to the discussion forums. DO NOT TAKE THIS COURSE!

By Rodrigo A

•

Aug 29, 2022

Very complete course that dive deep into the functioning of TFX pipelines, orchestrations, CI/CD, showing tools and resources we can use to automate the maintence of ML process. Thank you all for this.

By Médéric H

•

Mar 7, 2021

This is a great course to learn how to apply MLOps principles in large scale machine learning projects. I'll refer to this course in the near future to bring its concepts to customer ML platforms.

By BHASKAR K

•

Sep 25, 2022

very nice and easy to undertand concepts , hope for more new such free contents , thanks to google , quicklab , coursera for providing this opportunities .

By GianPiero P

•

Mar 22, 2021

Very good, thanks!

By devindu m

•

Mar 28, 2024

Labs don't work! week 2,3 and 4.