Excellent course

Ernesto Anaya

Great course with lots of practical tips and advice for Machine Learning Engineer. But, why are you hiding ungraded labs? Can we learn something of off the labs without paying money. If they are ungraded, can you at least make them available for students to try them locally on our computers. This whole effort of teaching is worthless if you always have to make profit. Do you think all of the people who are hungry to learn can pay you? You can charge for certificates, but don't charges for sharing labs.

It teaches a lot about the basics of ML in industrial production. I find there is a lack of lab work and real-world examples. The provided lab works do not go in-depth teaching the concepts discussed in the course. They seemed superficial. More graded quizzes are needed to test the pupils and make the courses more interactive.

The content of the course is really good and gives a great brief about MLOps. However, there are very few lab exercises and all of them are ungraded. Moreover, all the lab exercises are in Jupyter Notebook. This is still a good introductory course to MLOps

It is really a nice conversation with Andrew Ng over some problems that you face when you try to put model on production, define projects and manage it. But, the frameworks that he proposes are totally general and this course has technical debts.

I have worked in AI powered healthcare imaging industry for some years. Most of concept mentioned are our daily routaine. It is good to catch them up with constructed courses but I was expecting more juciy.

Consice and Vocational , especial to those working on unstructured data. I enjoyed it. Thanks

Weeks 2 and 3 were too much into theory. There wasn't much practice and application.

1) too shallow. 2) too many repeating content. Overall I don't feel very helpful

Much more a high level course respect to what i expected

very boring and should not be part of specialization

the course was full of videos without practice

I wish we had more practical exercises

too high level and really basic

its very good experience

tensorflow..

Well, I am a big fan of Andrew Ng, his initial ML course is what kickstarted my career change from a computer scientist to an established data scientist, I quite liked the Deep Learning Specalization, but this course is absolutely not what I hoped it would be. Explaining what a confusion matrix is in an MLOps course? Explaining precision, recall and F1 score? Come on. That is not content I want to hear about when paying for an MLOps course. Data augmentation and feature engineering? Also, not MLOps topics. A lot of important topics are briefly discussed, but not in detail. Quite a bit of content is rehashed from the Deep Learning Specalization. Good content, but this course is not the right place for that.

I was quite disappointed, as it feel that this 1 week course could have been summarised in a 15min article: there are a lot of generalities, repetitions... and no hands-on assignments where you are effectively expected to code something (which is, to my point, fundamental to get something of ML mooc).

We need more practical graded exercise lab to hone our technical skills, the labs in most cases are easy and not job specific

Coursera refuses to issue the specialisation certificate even though I spent over 300 euros and completed all courses. If I was told this beforehand, I would not spend anything on the course. The customer support team has not been helpful.

too much theory, the course could include some lab practices and be more fun and memorable