Interesting and good content, but the instructor is nowhere near the level of Andrew Ng. I had trouble understanding many topics which were not properly introduced. Also, tensorflow tfx documentation is not that good, so it's hard to understand it.

I have learned many interesting things. Thank you.

Nvertheless, teacher's speech is not well organized. He sometimes talks about a concept, then about another and then come back to the first one. And there are some repetitions.

I would really enjoy if we made it bottom up, and implement more stuff like this in pure python. Tensorflow is deprecated and very tiring. Very non pythonic. Otherwise the material and teaching is great.

Not a very good instructor. Concepts are very useful, but that is all you will get. It covers basic Data Lifecycle without going in-depth on any topic. Instructor is always rushing through content.

It was very boring to hear a lot of topics. I think first two weeks were worst to hear, last two weeks were really good.

Assignments quizes were sometimes very unlogical.

I really think that the instructor could have provided a better structured program with a student-focus, rather than a monotonic description of a process.

There is quite a bit of support coding that is required to perform many of the tasks in the final lab. It is what it is and I got through it.

Great disappointment after the first course. It comes with valuable knowledge, but I can't say it prepares for real world scenarios

Content is difficult to relate with, feels disconnected between modules and between different chapters.

Didn't find the labs especially practical, some presentations felt bogged down by definitions.

too much duplicated contents around lab and task. better to be short by removing duplication

No me encantó porque claramente hay que tener experiencia trabajando con tsx

not good as machine learning , it's all about using library from google

General introduction but skips over a lot of detail.

the instructor is very quickly

It is too much Google oriented

not what I thought

I found this course very dissapointing, especially in comparison to the previous course that this expands on. Given the reputation of the speaker, I was expecting a higher standard.

To begin with, there is far too much focus on TensorFlow. The concepts in this course are important to know, however they are briefly introduced in the videos, which are followed by a TensorFlow coding lab. The key information is hidden behind what are called "programming assignments", which unfortunately are nothing more than regurgitating TensorFlow code. For week 3, the videos total 40 minutes, and for week 4, 31 minutes. This course would be improved by spending more time explaining the MLOps principles.

Many of the principles encountered in this course I have already been practising during my PhD, choosing to handcode basic pipelines to automate my ML analysis. I would say that while this course is useful, knowing how to automate a machine learning pipeline does not prepare you for the working world. Without understanding exactly what is being done when you run each TensorFlow command, you will not be able to understand what you are doing and this will limit the impact of the work produced. I have learned more through my own work than I have during this course.

There are alternatives to TensorFlow for automating ML pipelines and the demonstrations are not hidden behind a paywall. For that matter, there are a large number of videos online that demonstrate the use of TensorFlow.

After the first wonderful course in this specialization, this one was quite a disappointment. While the topic is quite dense, the material covered in this course was very superficial and served only to sponsor TFX even though there are many other tools in this landscape. A more fitting name for this course would be "An introduction to TFX for the Machine Learning Data Lifecycle in Production:"

I was pretty disappointed in this course, particularly compared to the previous Andrew Ng course in this specialization. The last course was very informative and general, where as this one felt like a sales pitch for TFX. I learned very little, especially since my focus is on deep learning, not the shallow, tabular data that was discussed here.

This course felt sloppy - many concepts not well explained or explained too quickly without detail, terms not defined but constantly referenced, I didn't feel like lectures prepared me for the labs and labs didn't provide enough guidance or explanation of the code. Not sure I really learned more than I could have from the tensorflow website.

Disappointed after 1st course in this specialization. In most cases slides were not useful - lecturer was just reading or paraphrasing them.

It felt more like Data pipelines in google environment rather than in universal ML data lifecycle.

Assignments/quizzes don't test or teach any real understanding of the material. Main focus is the non generalizable syntax of txf.

Not very helpful or clear. can get repetitive sometimes. i still feel i dont fully grasp what is being said and the use cases

The instructor just reads the slides. No good explanation is provided.

Not comparable to Andrew Ng's courses by any means.