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.
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.
автор: Trinh H N•
Great course with useful exercises to get learner familiar with ML Data pipeline using TensorFlow Extended!
автор: Flurin G•
Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!
автор: Fernandes M R•
Its good, I think was a little difficult because TensorFlow, but it was very explicative.
автор: Luis S S•
Excellent course. Theory and practice well combined, to fit diverse curiositiy levels.
автор: Han B•
instruction on debugging jupyter and submission issue is important for learners
автор: Tom v D•
This was my first course with Robert, which was a very pleasant experience.
автор: Zanuar E R•
It is really good course, the detail explanation of Data LifeCycle in TFX!
автор: Walt H•
You can immediately apply everything you learn in this course!
автор: Hieu D T•
Some questions are difficult. Lots of new terms. Great course!
автор: Pierre-Alexandre P•
Very good training about data lifecycle for ML projects
автор: Meng C•
Great overview and labs for cutting-edge TFX platform.
автор: David B M•
Podría ser cool el modo dark en los laboratorios
автор: Chandan k•
A good course indeed to pursue my dream job !
автор: Thành H Đ T•
it's very nice. thank you so much
автор: Shannen L•
very helpful for ml engineers
автор: Shan-Jyun W•
Great course! Very Useful!
автор: RISHABH S•
Great practice exercises!
автор: BRAMWEL O•
Great hands-on learning.
автор: EMO S L•
автор: Viktor K•
автор: Jennifer K•
This 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•
To 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•
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.