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

4.8
étoiles
1,652 évaluations
289 avis

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

RG

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

IU

5 déc. 2021

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

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301 - 325 sur 330 Avis pour Introduction to Machine Learning in Production

par NITHIN J

11 oct. 2021

Useful course for understanding how ML works in production in an iterative approach.

par Ahmed M A

1 juil. 2021

Very comprenshive course summarizes the concept of Data-centric approach in MLOps

par Myrzakhan N

6 juil. 2021

T​his course was very useful on planning ml model deployment lifecycle!

par Jaret A

1 juil. 2021

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

par Umberto S

23 juil. 2021

Really clear explanation about foundamentals of ML in real world

par Dong P

27 déc. 2021

Great course for establishing real Machine Learning projects

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 deeplearning.ai, 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 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.