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Вернуться к Machine Learning Data Lifecycle in Production

Отзывы учащихся о курсе Machine Learning Data Lifecycle in Production от партнера deeplearning.ai

4.4
звезд
Оценки: 421
Рецензии: 76

О курсе

In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. 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: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types...

Лучшие рецензии

SC

2 июля 2021 г.

Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.

AW

13 окт. 2021 г.

It is a very informative course. I learned a lot about data, metadata, schema and feature engineering, Also, Robert Crowe sir is a very good teacher.

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26–50 из 82 отзывов о курсе Machine Learning Data Lifecycle in Production

автор: Antonis S

9 мар. 2022 г.

+ New cool way of working with many possibilities

-Many new concepts and code with no clear connection to the "known" way of working.

-New code concepts not very clearly explained Urgent suggestions for improvement: Make the new concepts and code clear to the audience. Connect the examples to the previous way of ML

автор: Hui J

4 янв. 2022 г.

A lot of the concepts are not well-explained. I feel like my mind is constantly drifting away when watching the video, to me, this course is more like a workshop/ads for tensorflow rather than explaining the data lifecycle properly.

автор: Reto A W

10 нояб. 2021 г.

I was not happy with the course. In the part 1 the lecturer showed a lot of real world example of developing big ML-systems. The lecturer for this course is more a library creator than a user of it. And therefore also it feels like an advertisement for tensorflow. Which is an odd combination for me. So it does not teach a lot of useful theory because it focuses on how tensorflow manages pipelines and not a lot about the concepts. But also the programming examples are very artificial examples taken from the tensorflow tutorials or documentation. What I liked in the first course was the practical view on a specific problem. The programming exercises I also did no like because I did not learn anything useful. I only "learned" to use tensorflow a bit. But the concepts implemented are so basic that they are not interesting at all. I am aware that this has to be like this if we are not expected to program for two day but I don't see the benefit for me of solving mandatory useless exercises. The result of this was: I was skipping through the videos in 2x and was solving the quizzes as fast as I could. Speaking of quizzes. There were quizzes asking questions never mentioned in the videos and once the quiz was posed before the video where the things were explained. Also the quizzes used unusual wording for concepts plus not clearly written questions. In the end there were some useful insights here and there but it was quite an effort for me to filter them out as my motivation was lacking after some time.

автор: Shreyas R C

21 июля 2021 г.

Best course for the professionals looking to upgrade there ML skills at production level! Thanks to the brilliant and wonderful course instructor.

автор: Youngjeon L

11 сент. 2021 г.

Nice, Awesome MLOps Pipeline with TFX! I recommend this course anyone who want to build ml pipeline! Good Luck! :)

автор: Nam H T

16 янв. 2022 г.

Great course with useful exercises to get learner familiar with ML Data pipeline using TensorFlow Extended!

автор: Fernandes M R

19 июня 2021 г.

Its good, I think was a little difficult because TensorFlow, but it was very explicative.

автор: Luis S S

10 сент. 2021 г.

E​xcellent course. Theory and practice well combined, to fit diverse curiositiy levels.

автор: Han B

15 янв. 2022 г.

instruction on debugging jupyter and submission issue is important for learners

автор: Tom v D

21 авг. 2021 г.

This was my first course with Robert, which was a very pleasant experience.

автор: Zanuar E

24 дек. 2021 г.

It is really good course, the detail explanation of Data LifeCycle in TFX!

автор: Walt H

8 сент. 2021 г.

Y​ou can immediately apply everything you learn in this course!

автор: Hieu D T

15 авг. 2021 г.

Some questions are difficult. Lots of new terms. Great course!

автор: Pierre-Alexandre P

9 июля 2021 г.

Very good training about data lifecycle for ML projects

автор: Meng C

13 янв. 2022 г.

Great overview and labs for cutting-edge TFX platform.

автор: David B M

26 дек. 2021 г.

Podría ser cool el modo dark en los laboratorios

автор: Chandan k

22 июня 2021 г.

A good course indeed to pursue my dream job !

автор: Thành H Đ T

8 авг. 2021 г.

it's very nice. thank you so much

автор: Shannen L

29 июля 2021 г.

very helpful for ml engineers

автор: Shan-Jyun W

15 янв. 2022 г.

Great course! Very Useful!

автор: RISHABH S

22 июля 2021 г.

Great practice exercises!

автор: BRAMWEL O

14 сент. 2021 г.

Great hands-on learning.

автор: Kamran S

25 мая 2022 г.

very informative

автор: Manuja

9 июня 2021 г.

Fantastic course

автор: Raspiani

19 авг. 2021 г.

Great, Thanks..