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Отзывы учащихся о курсе MLOps (Machine Learning Operations) Fundamentals от партнера Google Cloud

Оценки: 382

О курсе

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: <<<...

Лучшие рецензии


11 мар. 2021 г.

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.


1 февр. 2021 г.

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 из 106 отзывов о курсе MLOps (Machine Learning Operations) Fundamentals

автор: Akshay P

22 февр. 2021 г.

Good Course

автор: András B

21 янв. 2021 г.

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.

автор: Anirban S

20 апр. 2021 г.

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!

автор: Connor O

9 июня 2021 г.

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.

автор: Miguel A C D

10 февр. 2021 г.

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.

автор: Serhiy P

23 февр. 2021 г.

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.

автор: thibault b

9 февр. 2021 г.

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.

автор: Abo Y

11 июня 2021 г.

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

автор: Kwodwo G

21 янв. 2021 г.

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

автор: Efim L

10 мар. 2021 г.

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

автор: Alexander R

26 мая 2021 г.

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

автор: Ning L

5 янв. 2023 г.

Good intro level course overall but lots of the hands on labs are out of date

автор: Mano M

9 февр. 2021 г.

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

автор: Arnaldo M

26 янв. 2021 г.

The structure and sequencing of this course is not clear

автор: simon

21 июля 2021 г.

Hard to follow

Assigment is not actually interesting

автор: Francisco L M

27 мая 2021 г.

Algunos laboratorios no funcionan adecuadamente

автор: Abd-El-Rahman A

5 июня 2021 г.

there was a lot of bugs in this course

автор: Holger H

29 мар. 2021 г.

The labs did not make any sense for me

автор: suppakarn w

5 июля 2021 г.

The last lab has too many error

автор: Saeed R

26 авг. 2021 г.

Good material but buggy labs

автор: Asha Y L

29 янв. 2021 г.

It was gud

автор: SULABHA J P

21 июля 2022 г.


автор: Zach T

15 февр. 2021 г.

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.

автор: shweta k

1 февр. 2021 г.

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.

автор: Hyunkil K

2 нояб. 2021 г.

so duplicated, poor lab