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Отзывы учащихся о курсе Machine Learning Modeling Pipelines in Production от партнера

Оценки: 302

О курсе

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability...

Лучшие рецензии


13 сент. 2021 г.

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


20 окт. 2021 г.

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.

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1–25 из 57 отзывов о курсе Machine Learning Modeling Pipelines in Production

автор: Folkert S

18 сент. 2021 г.

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.

автор: Panagiotis S

25 янв. 2022 г.

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.

автор: Xavier D

13 окт. 2021 г.

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

автор: Arturo M

26 июня 2022 г.

I'm a big fan of Andrew Ng's ML courses. However, I'm very dissapointed with this one, for several reasons.

First, the instructur is not nearly as engaging as Andrew Ng himself. Most of the time he basically reads through the slides in a monotous way.

Second, the course tries to cover too many concepts. Instead of selecting a few core topics and explaining them in detail, the course seems like an ennumeration of ML concepts. Most of the times, the explanations are way to shallow to be of practical use.

Third, the exercises are quite poor. Most of them are just plain Google Cloud tutorials on Quicklabs.

автор: Peter W

9 авг. 2021 г.

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.

автор: Nithiwat S

29 июня 2022 г.

Just like the previous course in the specialization by the same instructor. He bascially reads from slides with little to no explanation. He barely explains any concepts, gives examples to help develop understanding. Some concept is unclear and poorly explained. Every single lecture, he just reads from scripts. It's very frustrating to learn. Coursera, in my view, is strong in delivery content that's more technical, more engaging, better explained technical concepts. But this course fails to deliver that. Watching many videos on Youtube is better than learning from this instructor. It is just terrible delivery, and I wish it was Andrew Ng who taught it. The only good part is the labs. Labs are well prepared and help with the study.

автор: Roger S P M

5 сент. 2021 г.

So Boring!

автор: Phillip G

14 авг. 2022 г.

Except for the huge amount of Google TFX sales talk, the biggest disappointment is that the instructor just reads the slides plainly without going over the key concepts in depth. Some of the concepts like PCA, they should be explained much more clearly, but what he does is just showing a rotating image and throwing out a few quizes. I mean, what's the point? I get far more knowledge from an Youtube video than mostly any of the concept explaination here. I have to double my time learning them, because there are rarely any good, down-to-earth explanations. Everything here is super fast, plain reading, and "Google stuff is the best".

автор: Ashwani K

7 авг. 2021 г.

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

автор: Stefan L

12 февр. 2022 г.

While 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.

автор: Hitesh K

18 июля 2021 г.

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.

автор: Cosmin K

16 авг. 2021 г.

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.

автор: Kiran K

22 июля 2021 г.

Good But More practical needed with theory

автор: Thành H Đ T

24 авг. 2021 г.

wow, Its very good

автор: Hieu D T

15 авг. 2021 г.

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.

автор: Andrei

9 сент. 2021 г.

need to improve the explanation of topics

автор: James A

7 дек. 2021 г.

This 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!

автор: Jonathan S R P

28 сент. 2021 г.

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 :)

автор: Travis H

19 дек. 2021 г.

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.

автор: Umberto S

29 авг. 2021 г.

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

автор: Jitendra S

14 сент. 2021 г.

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

автор: Melanie J B

21 окт. 2021 г.

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.

автор: Nhan N L

21 сент. 2021 г.

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

автор: Mario T

5 сент. 2021 г.

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

автор: Ankit P

26 апр. 2022 г.

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