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Back to Machine Learning Operations (MLOps): Getting Started

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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101 - 113 of 113 Reviews for Machine Learning Operations (MLOps): Getting Started

By Asha Y L

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Jan 29, 2021

It was gud

By SULABHA J P

•

Jul 21, 2022

good

By Zach T

•

Feb 15, 2021

Course focuses entirely too much on Google's managed offerings, many of which are still in Beta. The course could significantly be improved by focusing on foundational knowledge such as deeper dives into containers, CI/CD processes, and should add a DataOps component which is completely skipped over.

By Shweta

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Feb 1, 2021

Lectures about theory concepts were good but should have also explained hands-on part. And qwicklabs sucked. Had high expectations from this course but it turned out to be very disappointing.

By Hyunkil K

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Nov 2, 2021

so duplicated, poor lab

By Imam S 0

•

Dec 21, 2021

ok

By Nils B

•

Jan 28, 2021

Cannot complete the course because the last lab requires you to create a git fork using the qwiklab account, but there is no way to receive the verification email on the account, which results in in inability to complete the course.

Also, every lab takes 15 minutes of loading time to even start which wastes a lot of time.

By Rowen R

•

Jan 31, 2021

When you have issues working out instructions and need help, The Tech Support is slow getting back to you, there's too many of them messaging you asking the same question about your problems. Plus the Instructions are poorly delivered which sets in a lot of confusion.

By Sunilkumar G

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Jan 21, 2021

Bad lab experience needs to give more precise information as it is taking too long to find small details and improper explanation of what is expected from the Learners. Hope that the improvements are made to ease the learning experience of future learners.

By Yermek I

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Oct 8, 2023

Not able to complete. Error: RuntimeError: Training failed with: code: 8 message: "The following quota metrics exceed quota limits: aiplatform.googleapis.com/custom_model_training_c2_cpus"

By dwight b

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Jan 16, 2021

Certain qwiklabs are not working and response from help desk states the problem is being addressed which has been over 3 weeks ago; no status as of yet.

By Rob L

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Oct 21, 2023

A neverending stream of jargon and self-promotion with occasional learning

By Yannick P

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Sep 22, 2021

You should really give simpler examples