Perfect!

love it

Great!

g​ood

good

nice

Good

nice

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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.

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).

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.

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 ;).

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.

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.

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.

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.

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

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

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...

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.

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)

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.

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