In this edition of Machine Learning Monthly, we take a look at how the world's biggest companies design machine learning-powered systems.

Machine Learning Monthly April 2021 - https://zerotomastery.io/blog/machine...

Links:
Learn ML (beginner-friendly courses I teach) - https://www.mrdbourke.com/ml-courses/
ML courses/books I recommend - https://www.mrdbourke.com/ml-resources/
Read my novel Charlie Walks - https://www.charliewalks.com

Connect elsewhere:
Web - https://dbourke.link/web
Twitch -   / mrdbourke  
ArXiv channel (past streams) -    / @danielbourkearxiv2821  
Get email updates on my work - https://dbourke.link/newsletter

Timestamps:
0:00 - Intro/hello
1:06 - My New TensorFlow for Deep Learning course has launched!
1:50 - Louis Francois’ What’s AI channel
3:17 - The best resources from the internet
3:40 - “What kind of machine learning project should I work on?”
4:10 - Machine Learning is an infrastructure problem
5:35 - Apple’s Human Interface Guidelines for Machine Learning
10:00 - Make it easy for people to correct frequent or predictable mistakes (move towards a data flywheel)
10:48 - Google’s People and AI Research (PAIR)
14:23 - Microsoft’s design guidelines for Human-AI interaction
18:03 - Facebook’s Field Guide to Machine Learning
19:55 - Spotify’s 3 Principles for Designing ML-powered products
22:09 - From Big Data to Good Data by Andrew Ng
23:48 - The Data Flywheel
25:45 - Where to learn how to design ML-powered systems
28:42 - Outro/goodbye 👋

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