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Вернуться к Machine Learning Data Lifecycle in Production

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

Оценки: 327
Рецензии: 63

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

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.

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 69 отзывов о курсе Machine Learning Data Lifecycle in Production

автор: Trinh H N

16 янв. 2022 г.

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

автор: Flurin G

8 сент. 2021 г.

Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!

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 R

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: Manuja

9 июня 2021 г.

Fantastic course

автор: Raspiani

19 авг. 2021 г.

Great, Thanks..

автор: EMO S L

20 сент. 2021 г.

Great content

автор: Viktor K

4 авг. 2021 г.


автор: Jennifer K

17 дек. 2021 г.

T​his is a very thorough introduction to data issues that arise when you go from proof-of-concept to project in production. It uses TensorFlow Extended components to illustrate workflow concepts, and the labs involve using these components in programming assignments. If you do all the ungraded labs, the programming assignments are quite easy.

автор: Ivan P

23 нояб. 2021 г.

T​o much emphasis on tensorflow, too few on underlying concepts, while we need it and alternative to TF. If the course was call "implementing <current course name> in TF" this would be fine, otherwise name is mileading. However, the course content is well structured and interesting, just 4 stars for a misleading name :)

автор: Søren J A

5 авг. 2021 г.

This is a nice course. I specifically like the focus on data and implementation of trained models.

ML is much more than getting models trained , real life data, data quality control and continuous model maintenance is key to having succes with ML in a real setting.