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

Оценки: 328
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: <<<...

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.

Фильтр по:

51–75 из 96 отзывов о курсе MLOps (Machine Learning Operations) Fundamentals


29 мая 2021 г.

I learnt new concepts in machine learning through google cloud platform and i am so happy for that. Thank you Coursera for giving this opportunity to gain Google certification and i learnt a lot about google cloud, Kubeflow, and had practical experience through graded external tool.

автор: Rakesh R

20 мая 2021 г.

Good course for overall view of Kubeflow orchestration, basics of kubeflow and containerisations and ML ops services available on GCP. Highly recommended if you wanna deploy models with best practices!

автор: Aditya K

21 февр. 2021 г.

Loved the content, labs, and regularly intervened quiz. The only suggestion is that, for Juniper Labs, a detailed video solution would have added more value to this course.

автор: Chauhan S

31 янв. 2021 г.

I think there should be more content about AIML can be better choice or preferable.

Otherwise all the things are okay I enjoyed this course and learn a lot.

ThankYou So much.

автор: Sushant K R

15 февр. 2021 г.

It is a good designed course, but I would prefer to have basic knowledge of Machine learning and data science in order to understand this course even much better.

автор: Taylor C

27 авг. 2021 г.

A few of the labs didn't work, had to contact support. Also would be good to point to documentation for various tools like kfp-cli

Otherwise good.

автор: Glen G

8 февр. 2021 г.

Content well written. Some lab issues. Resolved but frustrating. Language processing a bit off on transcribed material from speakers.

автор: Al M B N

21 янв. 2021 г.

The course is quite educational, yet the lab material can sometimes be confusing, especially for beginner users

автор: Roberto C L

6 янв. 2022 г.

I​t's ok. There are example notebooks to understand the code. The pricing part is missing.

автор: Prateek G

3 июня 2021 г.

It was good experience learning about the deployment process of ML application on GCP.

автор: Jorge M

17 июня 2021 г.

Needs to cover the subject in greater detail

автор: anns

21 дек. 2021 г.

It's a good tutorial for beginner

автор: Maria Y

25 мар. 2021 г.

Good learning experience.

автор: Elhassan A

28 февр. 2021 г.

The labs are so important

автор: NISHAN K M

4 февр. 2021 г.

learned something new

автор: Srinivasan P V

31 янв. 2021 г.

Material is good

автор: Akshay P

22 февр. 2021 г.

Good Course

автор: Walter H

8 сент. 2021 г.

while this course teaches some useful skills, in particular how to to offload ML workloads to GCP, and introduces Kubeflow well, it doesn't go into enough depth to really let the students master the material. It doesn't help that Kubeflow (and its GCP implementation) are fundamentally fairly complicated technologies that compete with other, more mature (but less specialized) tools like Airflow. All in all, a good starting point, but don't expect to master the material - further study will be required. This course only scratches the surface.

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.