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Вернуться к ML Pipelines on Google Cloud

Отзывы учащихся о курсе ML Pipelines on Google Cloud от партнера Google Cloud

Оценки: 38
Рецензии: 7

О курсе

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<...

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1–7 из 7 отзывов о курсе ML Pipelines on Google Cloud

автор: Daniel L

11 апр. 2021 г.

The Good: The instructors were clear and concise and provided just the right amount of context and justification for the concepts they presented. The Bad: The labs were very burdensome, unstable, and provided very little toward the learning for the course. Even worse: The Qwiklabs experience, having to interact with their support multiple times, was unbelievably frustrating; Bad enough in fact that I doubt I would ever take another Coursera course if it is coupled with Qwiklabs. Very unfortunate.

автор: Gulshat K

2 нояб. 2021 г.

Quicklabs support is poor. Labs fail to implement (labs in weeks 2 and 3). Instructions are not clear for some labs. This is the las module in this specialization. It is pity that the last module wasnt implemented well and spoils the whole impression for the entire course.

автор: Javier J

5 окт. 2021 г.

The labs are broken and you cannot complete this certificate following instructions.

And if you don't follow instructions you can get banned from the labs.

Join this course at your own risk.

автор: Pierre-Yves D

4 дек. 2021 г.

After going that far in this specialization, the lab week 2 asking to bind a trigger to github was the end of my journey.

автор: Kurapati V S M K

30 нояб. 2021 г.

The course is fine but guided labs little out of sync for the content.

автор: Médéric H

7 мар. 2021 г.

This is a great course to learn how to apply MLOps principles in large scale machine learning projects. I'll refer to this course in the near future to bring its concepts to customer ML platforms.

автор: GianPiero P

22 мар. 2021 г.

Very good, thanks!