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Learner Reviews & Feedback for Production Machine Learning Systems by Google Cloud

4.6
stars
960 ratings

About the Course

In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions....

Top reviews

BA

Sep 22, 2020

Unlike pure technical courses, this one specially packs you with knowledge that you may find yourself face to. The course is really well designed and the content is crystal clear, just Awesome !

AK

Dec 6, 2018

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

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76 - 100 of 105 Reviews for Production Machine Learning Systems

By Bhargav D

May 8, 2020

Some modules were amazing and deserved more than 5 stars, other were a little too much. Specifically, everything was great until Kubeflow which I felt was not covered well and the demo not one thing was clear. Very bad demo! Please restructure that demo

By Namita D

Oct 9, 2020

The course provides insight into applying Machine Learning system in actual environment, with real-life examples and lab assistance.

By Renato C

Mar 21, 2020

The content and instructors are great. However it shoud not be a two weeks course, as the amount of topics is large.

By Andrei L

May 18, 2020

The course is good. but i would appreciate if it would give more details and more coding exercises

By Steven P G

Aug 11, 2019

Es un curso algo confuso que requiere bastante tiempo para comprender las tematicas

By Mahmmoud M

Mar 7, 2020

very very useful topics to improve and deploying machine learning for productions.

By Peng L

Jan 29, 2021

Course is a bit outdated. E.g., KubeFlow section and references to ML Engine.

By Flayson J P S

Jul 24, 2023

Curso incriável, matérias bem explicadas, conteúdo super, detalhados.

By Nikhileshkumar I

Sep 1, 2019

Great. Ksonnet is not active. Vdo should talk about it.

By Devpal S A

Jan 11, 2024

It was Good Overall. Got something new to learn.

By Didigam N

Apr 11, 2020

content is nice but too fast

By hemant k

Nov 25, 2018

Very Informative.

By PRATHAMESH V B

Jun 11, 2020

Good Experience

By Santosh L

Apr 8, 2020

Excellent

By VIGNESHKUMAR R

Dec 28, 2019

GOOD

By choisungwook

Jul 2, 2019

good

By 길경완

Jun 29, 2019

well

By Rebecca S

Jan 19, 2021

Some of the content was really interesting, particularly about the hybrid ML systems, dynamically training models, distributed training and data parallelism, but overall, the information was mostly high level with few exercises or labs to delve into actually designing and implementing this stuff. I wrote lots of notes and found myself asking 'how' a lot with no answer. It also felt like I was constantly being pitched to buy and use GCP services. And finally, to actually build a product off these tools that could be considered 'production', I don't think having a bunch of notebooks and random CLI commands to launch stuff is the most robust and traceable architecture. Great presenters though, really liked your style folks!

By Harold M

Nov 8, 2018

Overall rating is 3 out of 5, as I expected more of the initial line in the first course. The optional Kubeflow lab has issues, as the ksonnet apply command line halts. Also, the last lab was expected to allow the student to code more, as this is the only way to make a person to gain more insights on the architecture.

By Lloyd P

Jan 6, 2019

The module on hybrid systems was weak. The time it would take to cover the material would be prohibitive so why do the intro that then apologize for not having the time to explain the material. Leave it out...

By JJ

May 16, 2019

While there is definitely some good and useful content in this course, not all of the material is useful. ~40% of the course felt like a sales pitch, at least to me.

By Ranjan P

Jul 19, 2023

I can't start next course after enrolling ML course ridiculous kindly start next course

By Junhwan Y

Jun 30, 2019

This course include deep contexts about Machine Learning. But, It's somewhat boring.

By 김유상

Jun 30, 2019

Some errors in Kubeflow quicklabs.

By KimNamho

Jul 9, 2019

thank you