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Learner Reviews & Feedback for Production Machine Learning Systems by Google Cloud

4.6
447 Bewertungen
45 Bewertungen

Über den Kurs

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-Bewertungen

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

AF

May 07, 2019

I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative

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26 - 45 of 45 Reviews for Production Machine Learning Systems

von Mina J

Jul 02, 2019

I walk through the whole system for the entire process of ML so that I could get insights on the forest

von 장해수

Jul 02, 2019

ExCellEnT!!!

von YoungkyunKim

Jul 03, 2019

thank you!

von Lee M

Jul 02, 2019

good

von Kate K

Jul 06, 2019

Really useful course

von Suresh R

Jul 13, 2019

good overall

von ANDRÉ L M P

Aug 06, 2019

Fantastic experience.

von Hemant D K

Nov 25, 2018

Very Informative.

von Carlos V

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.

von 길경완

Jun 30, 2019

well

von choisungwook

Jul 02, 2019

good

von Steven P G

Aug 11, 2019

Es un curso algo confuso que requiere bastante tiempo para comprender las tematicas

von Harold L M M

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.

von Lloyd P

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...

von Mirko J R

Apr 02, 2019

Very theoretical.

von JJ

May 16, 2019

While there is definitely some good and useful content in this course, not all of the material is useful. ~40% of the course felt like a sales pitch, at least to me.

von 김유상

Jun 30, 2019

Some errors in Kubeflow quicklabs.

von Junhwan Y

Jun 30, 2019

This course include deep contexts about Machine Learning. But, It's somewhat boring.

von KimNamho

Jul 10, 2019

thank you

von Maxim

Jul 05, 2019

This specialization consists of 5 courses:

Course1: End-to-End Machine Learning with TensorFlow on GCP

Course2: Production Machine Learning Systems

Course3: Image Understanding with TensorFlow on GCP

Course4: Sequence Models for Time Series and Natural Language Processing

Course5: Recommendation Systems with TensorFlow on GCP

In specialization's FAQ say nothing about "audit" option. Are You know what is it ? "Audit" means that You can use course video material even after You subscriptions ended.

By fact, only "Course 1" has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization:

courses 2-5 has special items in FAQ:

"Why can’t I audit this course?

This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

"

"Who have paid" means that after You subscriptions ended, you lost access to video materials in this courses.

p.s.

1 star only for "Audit", content and lecturers are rated higher - at least 4 stars