Chevron Left
Back to Production Machine Learning Systems

Production Machine Learning Systems, Google Cloud

4.5
121 ratings
15 reviews

About this Course

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...

Top reviews

By 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

Filter by:

15 Reviews

By Alexander Kulikov

Feb 10, 2019

excellent

By bhadresh savani

Jan 23, 2019

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

By Mark Davey

Jan 15, 2019

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

By 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...

By Raja Ranjith Garikapati

Dec 08, 2018

Very informative on production systems....

By 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

By Hemant Devidas Kshirsagar

Nov 25, 2018

Very Informative.

By Michael Feldman

Nov 11, 2018

wow gcp michael feldman

By 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.

By 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.