From Python for Big Datasets by John T. Wolohan
By Dan Bergh Johnsson, Daniel Deogun, and Daniel Sawano
This article delves into DDD and models: what they are, how they relate, and how models work within Domain-Driven Design.
From Production-Ready Serverless by Yan Cui
From Spark in Action, Second Edition by Jean George Perrin
From Graph-Powered Machine Learning by Alessandro Negro
Six Questions for Kim Falk author of Practical Recommender Systems
By Frances Lefkowitz
Kim Falk is a Copenhagen-based data scientist who works with machine learning and recommender systems. Keep up with him @kimfalk on Twitter.
Chapters selected by Marcello Seri
Six Questions for Jon Skeet, author of C# In Depth, 4th Edition
Jon Skeet (@jonskeet) is a senior software engineer at Google, London and a recognized authority on Java and C#. He is the top contributor to Stack Overflow.
From Machine Learning for Business by Doug Hudgeon and Richard Nichol
In this article, you’ll see how SageMaker and the Random Cut Forest algorithm can be used to create a model that will highlight the invoice lines that Brett should query with the law firm. The result will be a repeatable process that Brett can apply to every invoice that will keep the lawyers working for his bank on their toes and will save the bank hundreds of thousands of dollars per year. Off we go!
From Data Science at Scale with Python and Dask by Jesse C. Daniel
This article delves into building linear models using Dask-ML.