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  • The Morning Paper Issue 7 - Experimentation, Optimisation and Learning

    For this edition of The Morning Paper Quarterly Review, Adrian Colyer has chosen a set of papers that illustrate what the data natives are up to: how they embed experimentation, optimisation, and learning into everything they do. If you thought continual delivery was the end game, for data natives this is just the necessary pre-requisite.

  • The InfoQ eMag: Introduction to Machine Learning

    InfoQ has curated a series of articles for this introduction to machine learning eMagazine, covering everything from the very basics of machine learning (what are typical classifiers and how do you measure their performance?) and production considerations (how do you deal with changing patterns in data after you’ve deployed your model?), to newer techniques in deep learning.

  • The Morning Paper Issue 4 - Computer Science Applied

    In this issue of The Morning Paper Quarterly Review Adrian Colyer looks at how simple testing can avoid catastrophic failures, symbolic reasoning vs. neural networks, how to infer a smartphone password via WiFi signals, how and why Facebook does load testing in production, and automated SLOs in enterprise clusters.

  • The Morning Paper Issue 3 - Computer Science Applied

    Adrian Colyer reviews five computer science papers which cover DBSherlock, how Google organises datasets, relaxing the majority quorum requirement in the Paxos Consensus algorithm, the key paper Netflix first looked to for principles on which to build its cloud architecture, and decomposing systems in modules.

  • The Morning Paper Quarterly Review Issue 2

    A summary of five CS papers chosen from the 55 that Adrian Colyer has reviewed for his Morning Paper blog during Q2 2016. Amongst the five papers in the magazine Colyer takes a look at how Facebook collect and analyse over 1 trillion data points per day across 2 billion unique time series, and the technology behind bots on Q&A systems like Siri, Cortana, Alexa et al.

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