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Production Machine Learning Systems, Google Cloud

4.5
Оценки: 121
Рецензии: 15

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

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Рецензии: 15

автор: Alexander Kulikov

Feb 10, 2019

excellent

автор: bhadresh savani

Jan 23, 2019

It was bit hard course but lab work was great and learn many production level consideration for ml systems.

автор: Mark Davey

Jan 15, 2019

Very practical which was nice. Thank you for adding the Quicklabs that helped a lot.

автор: Lloyd Palum

Jan 06, 2019

The module on hybrid systems was weak. The time it would take to cover the material would be prohibitive so why do the intro that then apologize for not having the time to explain the material. Leave it out...

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.