Chevron Left
Retour à Introduction to Machine Learning in Production

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

4.8
étoiles
1,776 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

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

TF

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.

Filtrer par :

301 - 325 sur 354 Avis pour Introduction to Machine Learning in Production

par Jennifer K

13 déc. 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.

par Emile S

21 juin 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.

par Roberto B

4 nov. 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.

par Ildefonso M

22 févr. 2022

Good general info but a bit basic for anyone who has already worked within the modelling pipeline. Nevertheless, Andrew is a great teacher and I did learn some new concepts and things to think about.

par Søren J A

25 juin 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

par Christian S

19 août 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.

par Sandeep U

17 mars 2022

Theoretically worth watching... but lack off hands-on excercises.... It would be more helpfull if there were any open sourse tools thought in the course...

par Lukas O

22 juil. 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.

par Dhruv N

5 juin 2022

Ecellent course. Although very focused on unstructured data and deep learning, so if you are from a structured data background, you might feel left out.

par Simon G

22 janv. 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)

par Magda K

14 juil. 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.

par akshay j

29 août 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.

par Leonardo d J S

23 oct. 2021

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

par Reet

18 nov. 2021

Too much story. It would be really great to see some codes and practical visual examples on real world issues.

par Hassan K

21 juin 2021

It's better the course has some coding assignments to comprehend the ideas which is shared by Prof. Ng.

par Nik B B

5 sept. 2021

The quizzes are meaningless, often ambiguous and the answers sometimes wrong. The videos are useful.

par Rajat G

6 sept. 2021

Some project plan / exercise / case study to be solved would have added more to the experience

par Anastasia P

21 févr. 2022

Maybe too basics for experienced folks. If you are the beginer this is a good class to take :)

par Filipe G

4 juil. 2021

Worked well in shifting the view from modelling into data. Worthwhile for that reason along.

par Justin L

7 juin 2021

S​o far this course has been pretty great. Nifty Labs with a lot of useful info and methods.

par Davide V

24 juin 2021

Quite basic, but still useful introduction to following courses of the specialization

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.