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Learner Reviews & Feedback for Machine Learning Operations (MLOps): Getting Started by Google Cloud

4.0
stars
413 ratings

About the Course

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: https://qwiklabs.com/terms_of_service <<<...

Top reviews

AM

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.

DM

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.

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51 - 75 of 113 Reviews for Machine Learning Operations (MLOps): Getting Started

By Marcio D

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Mar 30, 2021

great

By Dr. S R

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Jun 26, 2021

good

By GOWTHAM G K

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Feb 6, 2021

good

By HARIRAM S

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Feb 6, 2021

good

By Avulla M

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Jan 26, 2021

good

By Vivek S

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Jun 12, 2021

MLOps fundamentals is a good introduction, great teachers! The only place that I feel needs improvement is the lab - it would be great if there is more time to do the exercises, the lab gets timed out at 2 hrs. Sometimes the lab instruction are not very clear. Also I would be happier If the instructors went through other build tools like Bazel, etc.... This course helped organize ML workflows and make it easier to experiment, deploy and iterate over model dev.... Overall a very good course!!

By Lavi S

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Feb 22, 2021

github repo used throughout the code will probably serve as a good template for my future projects. The quizzes are on the easy end. The labs can be achieved by a series of copy+paste. Some give the full points for just opening the notebooks without even running them (same set of steps in two of the labs that only differ in notebook content). Feels like I have a lot to go before I'll be able to use these tools for my own tasks. Nevertheless - got to start somewhere.

By Kenneth H

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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.

By Ronit S

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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.

THANKS :)

Ronit Sagar

By RUCHITHA G

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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.

By Alireza " S

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Jan 25, 2023

Very good way to get updated on all the MLOps stacks by GCP. However, the information is super compressed and there are many topics that one has to cover.

Some basic DevOps topics will also be taught during the first week, so you can try to partake even if you have no DevOps knowledge.

By Jeremy L

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May 24, 2023

The overview of ML Ops as a discipline is very very good, and contrasts with Dev Ops and Software Engineering as co-disciplines. The lab is fairly basic but a decent introduction, however little time is spent explaining its application to ML Ops.

By Rakesh R

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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!

By Aditya K

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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.

By Chauhan S

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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.

By Sushant K R

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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.

By Taylor C

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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.

By Glen G

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Feb 8, 2021

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

By Al M B N

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Jan 21, 2021

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

By Roberto C L

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Jan 6, 2022

It's ok. There are example notebooks to understand the code. The pricing part is missing.

By Jag S

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Jul 17, 2023

Good starter on basic MLOps on GCP for those who want a quick dive and a hands on project

By Prateek G

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Jun 3, 2021

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

By surena

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Apr 13, 2022

I miss a chapter on automating monitoring models when metrics diverge

By Jorge M

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Jun 17, 2021

Needs to cover the subject in greater detail

By anns

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Dec 21, 2021

It's a good tutorial for beginner