Chevron Left
Вернуться к MLOps (Machine Learning Operations) Fundamentals

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

4.0
звезд
Оценки: 323
Рецензии: 96

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

Лучшие рецензии

AM
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.

DM
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.

Фильтр по:

76–96 из 96 отзывов о курсе MLOps (Machine Learning Operations) Fundamentals

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: Ефим Л

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: 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: 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

автор: Imam S 0

21 дек. 2021 г.

ok

автор: Nils B

28 янв. 2021 г.

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.

автор: Rowen R

31 янв. 2021 г.

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.

автор: Sunilkumar G

21 янв. 2021 г.

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.

автор: cor

16 янв. 2021 г.

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.

автор: Yannick P

22 сент. 2021 г.

You should really give simpler examples