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.

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.

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.

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

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

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

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

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

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

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

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

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