Modern Data Solutions with Python

From Python for Big Datasets by John T. Wolohan


Models as a Tool for Deeper Insight

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.

Armoring Lambda for Production

prod_ready_serv
From Production-Ready Serverless by Yan Cui

The Inner Workings of Spark

spark_in_act

From Spark in Action, Second Edition by Jean George Perrin

The Magic of Graphs and Machine Learning

PMachineLearning-MM
From Graph-Powered Machine Learning by Alessandro Negro

Interview: Taking Advice from a Machine

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.

Free eBook: Exploring Haskell

ExploringHaskell

Chapters selected by Marcello Seri

Tracking an Evolving Language

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.

The Random Cut Forest Algorithm

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!

Building Linear Models with Dask ML

From Data Science at Scale with Python and Dask by Jesse C. Daniel

This article delves into building linear models using Dask-ML.

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