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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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76 - 100 of 113 Reviews for Machine Learning Operations (MLOps): Getting Started

By Maria Y

•

Mar 25, 2021

Good learning experience.

By Elhassan A

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Feb 28, 2021

The labs are so important

By NISHAN K M

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Feb 4, 2021

learned something new

By Srinivasan P V

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Jan 31, 2021

Material is good

By Akshay P

•

Feb 22, 2021

Good Course

By 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.

By Anirban S

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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!

By Chima K P

•

Mar 21, 2023

A good course to get started with MLOps. The reason why I think the course deserves not more than 3 stars is that it lacks the depth that is needed to aid a better understanding of the concepts and components discussed. Overall, it's a good place to start and gain intuition about MLOps.

By Connor O

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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.

By Miguel A C D

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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.

By Serhiy P

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Feb 23, 2021

Even though class was taught by instructors from Google, the quality of tech around it was not Google-like. The labs in two week have serious issues once the pre-requisite steps are complete and experimental/fun//learning part of the lab begins.

By Thibault B

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Feb 9, 2021

Donne une bonne vue théorique du MLOps sur GCP mais la pratique est moyenne. Il manque un réel cas d'étude pour solidifier les acquis.

By Abo Y

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Jun 11, 2021

good content, but labs tend. To fail and debugging/support is not fantastic, forums dont have so. Many posts to support Either.

By Kwodwo A G

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Jan 21, 2021

The Labs took a lot of the promise the course had. It was a good time overall. Learnt a lot that requires further attention.

By Efim L

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Mar 10, 2021

Lab infrastructure doesn't work. For example, folders "mlops-on-gcp" was hidden. So, I can't touch labs properly :(

By Alexander R

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May 26, 2021

Some of the labs works only with out of course workarounds, the course needs updating.

By Ning L

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Jan 5, 2023

Good intro level course overall but lots of the hands on labs are out of date

By Mano

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Feb 9, 2021

Good but in lastest lab on chapter3 should work with git also.

By Arnaldo M

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Jan 26, 2021

The structure and sequencing of this course is not clear

By simon

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Jul 21, 2021

Hard to follow

Assigment is not actually interesting

By Francisco L M

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May 27, 2021

Algunos laboratorios no funcionan adecuadamente

By Abd-El-Rahman A

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Jun 5, 2021

there was a lot of bugs in this course

By Holger H

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Mar 29, 2021

The labs did not make any sense for me

By suppakarn w

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Jul 5, 2021

The last lab has too many error

By Saeed R

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Aug 26, 2021

Good material but buggy labs