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

4.8
estrellas
1,269 calificaciones
226 reseña

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

IU
5 de dic. de 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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201 - 225 de 259 revisiones para Introduction to Machine Learning in Production

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 Trung N H

21 de sep. de 2021

g​ood

por Duc A L

11 de oct. de 2021

Good

por Willah m

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 ;).

por Jennifer K

13 de dic. de 2021

T​his course offers a lot of practical advice, the kind you won't find in most machine learning courses and the kind that you'll use on a day-to-day basis in your career as a data scientist. It's quite easy to follow and appropriate for beginners and non-technical students.

por Emile S

21 de jun. de 2021

A​ndrew Ng's insights on the ML field are always very relevant. I would have liked to learn more about the different MLOps tools available out there, but I understand this might not be this class's objective, which is really about offering a general overview on the topic.

por Roberto B

4 de nov. de 2021

Good course to learn the jargon of ML-OPS will definetly give you good pointers to think about things you encounter daily on the job as a data scientist or ML engineer. Wish it would have been a little more technical.

por Søren J A

25 de jun. de 2021

I like the acknowledgement of the importance of data quality. Machine learning is much more than just training models. Real benefits can only be achieved when moving to real life data

por Christian S

19 de ago. de 2021

Very well explained. However, I feel that problems related to structured data are underrepresented though being extremely relevant for business in an enterprise context.

por Lukas O

22 de jul. de 2021

The methods are generally helpful. I would have liked more overview of available paid and open source tools, even if no specific recommendations are made.

por Simon G

22 de ene. de 2022

I​ntroduction to MLOps of deeplearning.ai, the course is a very good introduction and overview (even though no IT skills are learned at this point)

por Magda K

14 de jul. de 2021

T​he course was very nice though for a Course that is part of a Specialization Course I found it to be too basic, even for an introduction.

por akshay j

29 de ago. de 2021

T​he concepts covered were really usefull and informative. But it could have been a chapter in a course rather than course in itself.

por Leonardo d J S

23 de oct. de 2021

It's great course for you learning about the process build model and problem that your model was created for its.