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学生对 Google 云端平台 提供的 MLOps (Machine Learning Operations) Fundamentals 的评价和反馈

348 个评分
100 条评论


This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models. This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...



Mar 11, 2021

The whole process of building the Kubeflow pipelines for MLOPs including the configuration part (what does into the Dockerfile, cloud build) has been explained fully.


Feb 1, 2021

Thank You , Coursera & Google, It was great session & learn some practical Aspects & fundamentals of ML. I hope it will help me in the future. Thank You.


51 - MLOps (Machine Learning Operations) Fundamentals 的 75 个评论(共 100 个)

创建者 Kenneth H

Jan 25, 2021

Enjoyed the course and it is very interesting. Although there is no formal "prerequisite" for the course, you will get much more if you have various basic concepts in AI/ML, python, Jupyter notebook, CI/CD & Google Cloud Build, K8S & GKE, YAML, Github - especially for the labs, I really enjoy them. You might see some people saying that they hit minor problems - in fact, those minor problems are also part of the learning.

创建者 Ronit S

Feb 16, 2021

It was amazing course and content. No doubt that you are best content provider for the study material. you are feeling the gap between industry and university. As a learner i also faced some difficulty which you need to review it once in "QUICKLABS" cluster creation.


Ronit Sagar


May 29, 2021

I learnt new concepts in machine learning through google cloud platform and i am so happy for that. Thank you Coursera for giving this opportunity to gain Google certification and i learnt a lot about google cloud, Kubeflow, and had practical experience through graded external tool.

创建者 Rakesh R

May 20, 2021

Good course for overall view of Kubeflow orchestration, basics of kubeflow and containerisations and ML ops services available on GCP. Highly recommended if you wanna deploy models with best practices!

创建者 Aditya K

Feb 21, 2021

Loved the content, labs, and regularly intervened quiz. The only suggestion is that, for Juniper Labs, a detailed video solution would have added more value to this course.

创建者 Chauhan S

Jan 31, 2021

I think there should be more content about AIML can be better choice or preferable.

Otherwise all the things are okay I enjoyed this course and learn a lot.

ThankYou So much.

创建者 Sushant K R

Feb 15, 2021

It is a good designed course, but I would prefer to have basic knowledge of Machine learning and data science in order to understand this course even much better.

创建者 Taylor C

Aug 27, 2021

A few of the labs didn't work, had to contact support. Also would be good to point to documentation for various tools like kfp-cli

Otherwise good.

创建者 Glen G

Feb 8, 2021

Content well written. Some lab issues. Resolved but frustrating. Language processing a bit off on transcribed material from speakers.

创建者 Al M B N

Jan 21, 2021

The course is quite educational, yet the lab material can sometimes be confusing, especially for beginner users

创建者 Roberto C L

Jan 6, 2022

I​t's ok. There are example notebooks to understand the code. The pricing part is missing.

创建者 Prateek G

Jun 3, 2021

It was good experience learning about the deployment process of ML application on GCP.

创建者 surena

Apr 13, 2022

I miss a chapter on automating monitoring models when metrics diverge

创建者 Jorge M

Jun 17, 2021

Needs to cover the subject in greater detail

创建者 anns

Dec 21, 2021

It's a good tutorial for beginner

创建者 Maria Y

Mar 25, 2021

Good learning experience.

创建者 Elhassan A

Feb 28, 2021

The labs are so important


Feb 4, 2021

learned something new

创建者 Srinivasan P V

Jan 31, 2021

Material is good

创建者 Akshay P

Feb 22, 2021

Good Course

创建者 Walter H

Sep 8, 2021

while this course teaches some useful skills, in particular how to to offload ML workloads to GCP, and introduces Kubeflow well, it doesn't go into enough depth to really let the students master the material. It doesn't help that Kubeflow (and its GCP implementation) are fundamentally fairly complicated technologies that compete with other, more mature (but less specialized) tools like Airflow. All in all, a good starting point, but don't expect to master the material - further study will be required. This course only scratches the surface.

创建者 András B

Jan 21, 2021

The course gives a nice overview, but either it should be more generic and fun, or more detailed and techy but also longer. Now it feels like its trying to do both and failing at it. It is a bit too condensed and boring on the practical parts, and most of the tasks can be solved with copy paste, and somehow I don't feel that the whole course motivated me into stop copy-pasting and instead actually learn these things. Several of the Qliklab workshops seem to be buggy.

创建者 Anirban S

Apr 20, 2021

The content is well designed and explained. The Hands-on Lab sessions need a lot of improvement. MLOps is implemented in a really complex manner (but that is more about a comparison between GCP and other providers). But for ramping up MLOps on GCP, this course is a really good starting point. Best of Luck!

创建者 Connor O

Jun 9, 2021

I took this so I could get better at Kubeflow on EKS (not Google Cloud) and it was not worth it. The Beginning is promising and the explanation of kubernetes was great, but then it quickly became not applicable. If you are using it for GCP then it may be worth while.

创建者 Miguel A C D

Feb 10, 2021

The labs are too basic, I expected to view how to use tools such as tensorboard with kfp, with the intention to track progress of the models. But more relevant is the lack of examples on how to train/hyperparameter-tunning using a kfp alone avoiding AI jobs tool.