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

4.0
звезд
Оценки: 355

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:

76–100 из 103 отзывов о курсе MLOps (Machine Learning Operations) Fundamentals

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: 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.

good

автор: Yağızhan A A

7 февр. 2022 г.

Videos are nice and good for learning new perspectives but there is a huge problem in this course. Labs (required to complete if you want certificate) are bugy and for example i need to wait for one lab problem to be solved if i want my certificate (which is going on for more than 2 weeks as i can see in forums). Overall, good quality videos but unexpectedly very poor technical management.

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: Joaquin S

2 авг. 2022 г.

Some labs are "unavaibale" in the Quicklabs portal. For example, in one Lab the portal says "Sorry, Using custom containers with AI Platform Training is currently unavailable"