I thought this course was ok. On the one hand, the theory that is taught is quite general and trivial, while on the other hand, the technical focus is mostly on Google's tools and deep learning. As getting machine learning to production is an advanced task and requires a broad set of skills, I would've, for instance, expected this course to be more on structured data. Also, most of the labs, especially the GCP ones, feel just like copying and pasting some commands, it's not that challenging and therefore I didn't learn a lot there.

Poor content on this course as well. A bit of intermediate machine learning concepts that we all have seen a thousand times and a bit of mlops. The instructor was always always reading only whats in the slide. Graded assignments on GCP were just copy/pasting the code and had no difficulty or needed any critical thinking or skills. Again focused ONLY on Tensorflow libraries that are incompatible with models from other libraries like Pytorch.

Covers a lot of content at a high level. One slight criticism is that the graded exercises focused on Google cloud and didnt require much thought. The ungraded labs on the other hand were quite interesting.

I find this course extremely hard to follow, some main and tricky concepts are only covered by a mere sentence in the lecture.

Some of the topics were too advanced and instructor assumes that we know those basics. It felt rush through little bit and more of reading slides then explaining at many places

So Boring!

So far the most informative course in this specialization. This course has actually taught me how different is ML in production than doing simple Ml stuff on notebook for academic or research purpose. You get to see the bigger picture, i.e, different and bigger constraints that needs to be addressed for deploying any model to be on systems, specially edge devices.

Great material, insigthful notebooks and a valuable review of numerous concepts and tools! The course set me on track with steps to take and pitfalls to evoid. Thank you! Now is practice and continous learning from my part.

Good But More practical needed with theory

wow, Its very good

A bit dependent on GCP, took me quite a decent amount of time to do network setting. You should use your own internet, do not use one behind corporate proxy like I did. Materials and guides are great.

need to improve the explanation of topics

W​hile this course conveys lots of interesting and relevant knowledge, it's labs do not. In fact, they are usually just copy/paste tasks based on existing GCP tutorials leveraging qwiklabs.

T​his course gives a very good overview of this topic. Very relevant to my day-to-day work (not academic, or too focused on research, etc.), it is well presented with good context and examples. Obviously a lot of hard work went into creating this course. Good learning experience, +1, thanks!

I strongly recommend this course to anyone interested in MlOps and how to manage a ML pipeline in production, i learn a lot about pipelines, distillation and interpretable models. Can wait to put all this knowledge in practice :)

Consistent with the other courses in the specialization for MLOps -- very insightful with good coverage of content that is relevant to the pipelines and automation requirements for proper production support.

Great course! One of the most clear and extended courses by DeepLearning.ai. I think It covers in an excellent way all topics to understand what MLOps is and how to approach it in the right way.

Excellent content and lectures from Mr. Robert . Thank you very much Sir for the excellent way of explaining these difficult topics . Thank you !!!

I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.

This course is helpful. Enrich my knowledge with data concepts, optimal high-performance model tools and model debugging.

Outstanding! Exceptionally informative. Makes me look way aheady how to implement ML pipelines, and how to analyze them.

It was really a wonderful and amazing course. I really learnt about what all goes in creating a successful ML project

Covers a lot of hot topics related to ML Modeling pipelines in production with great breadth and depth.

T​his is very helpful course to understand the life of model specially after its deployment.

This course filled in some black holes in my knowledge and I found it very helpful.