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学生对 提供的 Introduction to Machine Learning in Production 的评价和反馈

1,785 个评分


In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline...



Jun 4, 2021

really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value


Aug 14, 2021

Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.


326 - Introduction to Machine Learning in Production 的 350 个评论(共 355 个)

创建者 Jaret A

Jul 1, 2021

Very interesting, a lot of new little concepts. I enjoy Andrew's tips.

创建者 Umberto S

Jul 23, 2021

Really clear explanation about foundamentals of ML in real world

创建者 Dong P

Dec 27, 2021

Great course for establishing real Machine Learning projects

创建者 Baurjan S

Jul 15, 2022

It's a great introduction course. But excessively easy.

创建者 Christian K

Jun 22, 2022

The content is great, but it could be condensed a lot!

创建者 Sudip C M

Mar 25, 2022

G​ood intro course on machine learning for production

创建者 Timothy G

Jul 10, 2021

Learn some additional information Mlop

创建者 changfuli

Jun 6, 2021

Would be great if comes with more labs

创建者 Kepchyck

Mar 22, 2022

It's cool, but it isn't for begginer

创建者 Simon A

Jul 27, 2021

Great, but needs more content !

创建者 Maria E

Jan 26, 2022

use a more hands on approach.

创建者 Mayank A

Jul 19, 2021

build foundations for MLOPs

创建者 Arman S

Apr 20, 2022

Good foundational course

创建者 yeison d

Sep 13, 2021

Amazing intro course

创建者 Javier P O

Apr 8, 2022

Great introduction!

创建者 davecote

Jan 18, 2022

light but usefull

创建者 shushanta p

Aug 1, 2021

Excellent course

创建者 Ernesto A

Jul 8, 2021

Ernesto Anaya

创建者 Enrique C

Jan 4, 2022

Good intro but it looks like in other courses from, while they teach you something, they also try to "sell" people a specific framework. In this case, they seem to be selling TFX. I still recall how they sold people the Trax library in the NLP specialization which has replaced Trax with huggingface. I take what is useful from these courses but I distrust their agenda.

创建者 Diego L

Jun 9, 2021

It is really a nice conversation with Andrew Ng over some problems that you face when you try to put model on production, define projects and manage it. But, the frameworks that he proposes are totally general and this course has technical debts.

创建者 jitao f

Aug 6, 2022

I have worked in AI powered healthcare imaging industry for some years. Most of concept mentioned are our daily routaine. It is good to catch them up with constructed courses but I was expecting more juciy.

创建者 yukongliang

Oct 3, 2021

boring and kind of wasting time. I mean, learning course 2-4 is enough ,why there is an extra "outline" course here? Also, the content is a duplication with Andrew's other courses in coursara.

创建者 Kenan M

Mar 11, 2022

Consice and Vocational , especial to those working on unstructured data. I enjoyed it. Thanks

创建者 Ravi A

Jan 11, 2022

G​ood overview of best practises, but still a bit too general and non-technical.

创建者 Matthew A

Dec 8, 2021

It seemed a little too general. I would've liked more labs.