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

1,788 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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276 - 300 sur 355 Avis pour Introduction to Machine Learning in Production

par Lahiru D

3 août 2022

Great work

par Mariana M S

24 juil. 2022


par Luis C M R

7 janv. 2022


par Koraldo K

6 août 2022


par Anugraha S

17 mars 2022



7 nov. 2021



7 sept. 2021

very good

par Thành H Đ T

29 juil. 2021

thank you

par Roberto C

19 juin 2021


par Ramil J

22 mai 2021


par Marcelo B

10 janv. 2022


par Vyacheslav K

25 mai 2021


par Khizar S

7 juin 2021

love it

par Amin T

5 juil. 2021


par Atif F

27 juin 2022


par Trung N H

21 sept. 2021


par Aman K d

21 avr. 2022


par Preetam G G

20 avr. 2022


par Duc A L

11 oct. 2021


par Willah M A

8 août 2021


par MohammadSadegh Z

17 juil. 2021

par Ajit k

27 mai 2021

T​his course help learner to gain key insights from one of the leader in AI field, for developing Machine leanring based applications. Course is keep more on discussion and thoughts than technical (more provided through ungraded lab exercies).


I​t would have been better if the graded labs was made part of the grading and had more lab exercies on fastAPI and other topics. (I think, the purpose of the course is to teach it to a larger audience including non-tech people).

I​ enjoyed and learned a lot from the course.


par Jeffrey B

28 déc. 2021

I was a little disappointed that this was heavily focused on unstructured data, but it was still a wonderful course. Many of the techniques of being "Data Centric" do not carry over as well to structured data. I am hoping I will hear more in the next courses of this specialization that address being data centric with structured data (which would seem to be more applicable to many business analytics cases).

par Cristian C H

21 oct. 2021

While the overall content of the course for ML LifeCycle is great, the examples and general assumptions are for supervised learning and labeled data, in some real scenarios, having labeled data is just not possible but by no means this indicates there is no possible AI solutions and models that give business value. So a little inclussion of unsupervised and semisupervised learning examples would help.