Chevron Left
Вернуться к Production Machine Learning Systems

Production Machine Learning Systems, Google Cloud

4.5
Оценки: 69
Рецензии: 11

Об этом курсе

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow...

Лучшие рецензии

автор: AK

Dec 07, 2018

It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations

Фильтр по:

Рецензии: 11

автор: Raja Ranjith Garikapati

Dec 08, 2018

Very informative on production systems....

автор: Artur Kuprijanov

Dec 07, 2018

It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations

автор: Hemant Devidas Kshirsagar

Nov 25, 2018

Very Informative.

автор: Michael Feldman

Nov 11, 2018

wow gcp michael feldman

автор: Carlos Viejo

Nov 11, 2018

This Course has excellent explanations and advice on how to move your models into production and make sure they are reliables and don't lose accuracy over time. The course illustrates how to use the entire ecosystem on GCP that is impressive, quite happy with the explanation and the expert's advice.

автор: Harold Lawrence Marzan Mercado

Nov 08, 2018

Overall rating is 3 out of 5, as I expected more of the initial line in the first course. The optional Kubeflow lab has issues, as the ksonnet apply command line halts. Also, the last lab was expected to allow the student to code more, as this is the only way to make a person to gain more insights on the architecture.

автор: Jun Wang

Nov 04, 2018

This course reveals some practical techniques in Production Machine Learning Systems. I like the real world examples introduced in this course.

автор: Cristobal Silva

Oct 29, 2018

While most of the content is sufficiently informative for a course, the implementation itself has too many issues: wrong videos in some modules, errors in quizzes, and so on. Once they organize the material properly, this course can definitely be 5 stars.

автор: Sinan Gabel

Oct 27, 2018

A lot of great production examples, labs and reviews but perhaps too many issues for a single course - however I understand that it was perhaps to provide an overview of the possibilities, a kind of "toolbox" for production ML.

автор: 林佳佑

Oct 20, 2018

very useful for consider data enigerring