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Opiniones y comentarios de aprendices correspondientes a Introduction to Machine Learning in Production por parte de deeplearning.ai

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Acerca del Curso

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...

Principales reseñas

RG

4 de jun. de 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

TF

14 de ago. de 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 de 354 revisiones para Introduction to Machine Learning in Production

por Mariana M S

24 de jul. de 2022

incredible

por Luis C M R

7 de ene. de 2022

Excellent!

por Koraldo K

6 de ago. de 2022

Fantastic

por Anugraha S

17 de mar. de 2022

Wonderful

por EDUARDO S

7 de nov. de 2021

Excellent

por VENKATA S R K

7 de sep. de 2021

very good

por Thành H Đ T

29 de jul. de 2021

thank you

por Roberto C

19 de jun. de 2021

Excellent

por Ramil J

22 de may. de 2021

Fantastic

por Marcelo B

10 de ene. de 2022

AMAZING!

por Vyacheslav K

25 de may. de 2021

Perfect!

por Khizar S

7 de jun. de 2021

love it

por Amin T

5 de jul. de 2021

Great!

por Atif F

27 de jun. de 2022

great

por Trung N H

21 de sep. de 2021

g​ood

por Aman K d

21 de abr. de 2022

good

por Preetam G G

20 de abr. de 2022

nice

por Duc A L

11 de oct. de 2021

Good

por Willah M A

8 de ago. de 2021

nice

por MohammadSadegh Z

17 de jul. de 2021

por Ajit k

27 de may. de 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).

S​uggestion:

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.

T​hanks.

por Jeffrey B

28 de dic. de 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).

por Cristian C H

21 de oct. de 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.

por Yoshihiro H

13 de oct. de 2021

This course is a practical guide for someone who's interested in developing ML models in real life, make use of it and maintain, improve, and support it for business needs. To those folks whos coming from an academic background and haven't seen the landscape of the use of ML models in real life, this course can be a really good starting point ;).