cloud free ebook Netflix AWS: Chaos Engineering

Chaos Engineering - O'Reilly Media

"With so many interacting components, the number of things that can go wrong in a distributed system is enormous. You’ll never be able to prevent all possible failure modes, but you can identify many of the weaknesses in your system before they’re triggered by these events. This report introduces you to Chaos Engineering, a method of experimenting on infrastructure that lets you expose weaknesses before they become a real problem.

Members of the Netflix team that developed Chaos Engineering explain how to apply these principles to your own system. By introducing controlled experiments, you’ll learn how emergent behavior from component interactions can cause your system to drift into an unsafe, chaotic state."

AWS Podcast | Listen & Learn About AWS
#238: Chaos Engineering and Architecture with Adrian Cockcroft | April 8, 2018

AWS re:Invent 2017: Digital Transformation (ARC219) - YouTube

related book:
Drift into Failure: From Hunting Broken Components to Understanding Complex Systems, Sidney Dekker, eBook - Amazon.com

also mentioned in podcast:

Open Source at AWS

Open Source at AWS @ GitHub

GitHub - awslabs/sockeye: Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

AI/ML/DL free ebook: Introduction to CNTK Succinctly

Free E-books | Syncfusion | Succinctly Series | Introduction to CNTK Succinctly

by James McCaffrey

"Microsoft CNTK (Cognitive Toolkit, formerly Computational Network Toolkit), an open source code framework, enables you to create feed-forward neural network time series prediction systems, convolutional neural network image classifiers, and other deep learning systems. In Introduction to CNTK Succinctly, author James McCaffrey offers instruction on the basics of installing and running CNTK, and also addresses machine-learning regression and classification techniques. Exercises and explanations are included in each chapter."

...

"There are several other deep learning frameworks. Microsoft CNTK is most similar to Google TensorFlow. In my opinion, CNTK is easier to program with than TensorFlow; however, all deep-learning frameworks have a fairly steep learning curve. Both CNTK and TensorFlow can be accessed by the Keras wrapper framework"