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Вернуться к Machine Learning Modeling Pipelines in Production

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

4.4
звезд
Оценки: 300

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

JS

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 !!!

MB

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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26–50 из 57 отзывов о курсе Machine Learning Modeling Pipelines in Production

автор: vadim m

4 авг. 2021 г.

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

автор: Reza M

14 сент. 2021 г.

This is very helpful course to understand the life of model specially after its deployment.

автор: Cees R

15 окт. 2021 г.

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

автор: amadou d

8 авг. 2021 г.

Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.

автор: Sri V D

6 янв. 2023 г.

Excellent introduction to pipelines for production ML.

автор: Daniel W

9 июня 2022 г.

Great course, probably the best in the specialisation.

автор: fernandes m

24 сент. 2021 г.

The first course of MLOps, and the best.

автор: Thiago P

1 февр. 2022 г.

Really liked the last week content

автор: MORUFU B

14 мая 2022 г.

This is a very detail course

автор: Илья В

9 сент. 2021 г.

great course, a lot of stuff

автор: Liang L

22 июля 2021 г.

Good content and hands on.

автор: Pedro C

3 окт. 2022 г.

Amazing course!

автор: Raspiani

28 авг. 2021 г.

Awesome Thanks

автор: 莫毅啸

24 дек. 2021 г.

haved fun!

автор: EMO S L

29 сент. 2021 г.

Nice !!!!

автор: Naveen K

23 нояб. 2022 г.

Good

автор: Vijay

20 нояб. 2022 г.

What's Good

- The selection of the topics for the programming assignments is outstanding - I am very experienced at completing QwikLab assignments with ML Pipelines and I still learned something new.

- The topic coverage is great - I learned a lot of things in the field that I was not aware of.

What can be improved

- The lectures are being read from notes and it's hard to listen to without increasing the playback speed to 2x. I think there should be more readings and the videos should be more engaging (i.e. like the GAN specialization's lectures).

- The quizzes should count as part of the grade. It's possible to complete the all QwikLab assignments within a couple hours and the entirely of the course completion credit is based on that.

автор: Fernando F

9 мар. 2022 г.

Very nice course. The reason I graded it as 4 (and not 5) was related to the educational value of the labs based on Google's console. Per se, the exercises were flawless but I felt like I was just running the steps without much understanding of what I was doing.

Yet, an awesome course. I learned a lot! Thank you very much!

автор: Carlos A L P

3 янв. 2022 г.

Great course, you can learn new concepts related to MLOps and new technologies like major Cloud vendors, packages and platforms like TensorFlow for the ML model. I would like to have more exercises to apply the various terms and processes seen during the course

автор: anand v

31 авг. 2022 г.

It covers a vast territory of material. However, there is plenty to learn in terms of concepts. Some of the graded labs can make you dizzy. Overall, it is worth the effort. Get a financial waiver if possible.

автор: Ioannis A

15 июня 2022 г.

There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.

автор: Jerry Z

4 апр. 2022 г.

Lots of hands-on exercises accompanying knowledge learned in this course 3, but could be difficult for someone without prior working knowledge on Google Cloud platform/services.

автор: Suet Y M

8 июня 2022 г.

The assignments are just quizes, and no practical programming exercise

автор: Ruan L D

19 нояб. 2021 г.

Good but I think that is much content for low time

автор: Shubhendu V

2 дек. 2022 г.

Many important concepts and topics of MLOps are discussed in this course, although there's too much focus on Tensorflow and associated libraries/tools. It would have been better to have hands-on with other MLOps open source libraries and tools.