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.

I have worked on a lot of Machine learning projects. It has helped me to organize my thoughts. How to access the best practices beforehand based on the size and type (structured/unstructured) dataset. I prefer more hands-on experience which was missing. I didn't learn exactly something new but it did help me understand how to look at and plan for a new Machine learning project.

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 ;).

I wish for graded programming assignments in this course as well.

Although much of the things talked about here are theoretical, a programming assignment here would be immensely useful to a beginner to get a practical idea about the related concepts being covered and referred here across the couse modules.

This 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.

Andrew 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.

Andrew Ng is exceptional when coming to explaining Machine Learning concepts, models and real life examples. A little gamification sets, more quizzes or minor tasks (like in the Stanford Machine Learning Matlab/R ones) would be nice to have :)

I liked the pace but it stayed too much on the surface for my purposes. The ungraded labs could have been graded with tasks to do and not prefilled. I think that took away some of the learning experience. Lets see what the next courses bring!

Excellent conceptual introduction to employment of ML models. This is the first course I have taken that focuses on the "outside of the box" of the individual model and working on development and improvement of a model.

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.

Good refresher if you already work in ML. A bit longish and could have been shortened.

I found the code provided useful to remind the use of Keras

In short, solid but not super mandatory

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.

Great course to get started into production, loved the philosophical discussions on building ML models for production as opposed to ones developed for industry

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.

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

The 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.

The 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.

Great course touching on some big topics in ML. I would like to add more labs and their video breakdown.