I know it's an introduction, but I got a bit disappointed. It's quite basic and even though it has some hands on notebooks, they're optional and you don't need to work on anything. Quizzes are easy, and I didn't have the feeling I learnt much. I'm still rating it with 3 because, well, it's Andrew Ng, and this his teaching is worth gold.

Most of the discussion was theoretical. Some useful knowledge but not useful for real world MLOps

I found the production part absent and is another ML course.

All pretty trivial

Thanks, Andrew!!!!! Your sharing real-life experiences was invaluable. This was super special as it has opened my eyes beyond the ML-code. I've realized what I have to do in my real job. I will spend more time on communicating with business teams to close the gaps on different metrics expectations. I will shift my mindset from code-centric to data-centric. I will check out my data before my team dives into the ML coding itself. Thanks, Andrew and the team!!

Awesome Course.... :) Really I enjoyed a lot. I completed this 3 weeks of course just in 4 days along with my office work (too much interesting).Very helpful... Very knowledgable... Thanks Andrew Ng for the course. A big thank to DeepLearning.AI team.

I give you the full review stars since I learned many new things that I did not pay attention to before, e.g.: I used to focus on models for many years instead of data.

I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!

Really good for anyone with strong background in DL and ML... And want to be able to start a real time project... Or lead a ML team

Very basic course

This course was one of best that I've taken regarding the ML. I think this course should be the starting point for each student who would like to pursue a career in ML and AI. Understanding the problem in the business context before jumping to the solution, understating the data in the same context, are the key ingredients for defining the success of a "product/service" involving AI.

It had some great advice for how to design a machine learning system. More practical examples would have been appreciated.

I think this was a good course in 2021, but a lot has changed since then. We need advice that is good today, not advice that was good in 2021. Coursera doesn't tell us how old these videos are. A few of the references are new, but most of them are from 2021 or earlier, so this makes me think most of the content is that old. The natural language processing in the labs is crude; it doesn't use LLMs or even transformers. Another major shortcoming is that there's no discussion forums, so there's no way to ask for help from the instructor or fellow students.

Not enough hands on experience

I liked how Andrew is able to simplify difficult and tricky concept without making you feel uncomfortable about lacking the knowledge. Everything is smooth and up to the point. In addition, the labs are interesting and highly related to the material. Overall, the concepts taught are very helpful and important to make you an real machine learning engineer not just a one who copy and paste bunch of theories, codes, ....etc.

If you have work on industry projects, you must have come across such scenarios described in the course. This course provides a structured way to analysis different situations arises during a ML project life-cycle and teaches way to make decisions which increases the chance of success. It is really helpful.

Very nice course! The field of MLOps is not so well documented and fortunately we have very experienced professionals able to share their expertise. The content is very clear and the examples provided by the professor are extremely insightful.

Even after having worked several years in the role of an MLE there were some useful ideas here and there that I'm excited about applying in the future. Overall, everything was very clear and understandable. I liked the lab about deployment.

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.

I found that the course is quite useful and practical. I enjoy a lot watching Andrew's Lectures especially when he used many examples from his previous projects in his career , giving good demonstration of common challenges in ML model development as well as maintenance/monitoring in production. The course is well designed and gives us a very clear foundation about Machine Learning in production.