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Avis et commentaires pour d'étudiants pour Introduction to Machine Learning in Production par

1,760 évaluations

À propos du cours

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...

Meilleurs avis


4 juin 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


14 août 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.

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326 - 350 sur 351 Avis pour Introduction to Machine Learning in Production

par Christian K

22 juin 2022

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

par Sudip C M

25 mars 2022

G​ood intro course on machine learning for production

par Timothy G

10 juil. 2021

Learn some additional information Mlop

par changfuli

6 juin 2021

Would be great if comes with more labs

par Kepchyck

22 mars 2022

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

par Simon A

27 juil. 2021

Great, but needs more content !

par Maria E

26 janv. 2022

use a more hands on approach.

par Mayank A

19 juil. 2021

build foundations for MLOPs

par Arman S

20 avr. 2022

Good foundational course

par yeison d

13 sept. 2021

Amazing intro course

par Javier P O

8 avr. 2022

Great introduction!

par davecote

18 janv. 2022

light but usefull

par shushanta p

1 août 2021

Excellent course

par Ernesto A

8 juil. 2021

Ernesto Anaya

par Enrique C

4 janv. 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.

par Diego L

9 juin 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.

par jitao f

6 août 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.

par yukongliang

3 oct. 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.

par Kenan M

11 mars 2022

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

par Ravi A

11 janv. 2022

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

par Matthew A

8 déc. 2021

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

par diego p

20 juil. 2021

Much more a high level course respect to what i expected

par Kiran R

25 sept. 2021

​very boring and should not be part of specialization

par Leandro K d O

13 juin 2021

I wish we had more practical exercises


7 sept. 2021

its very good experience