From The Art of Network Penetration Testing by Royce Davis
From High-Performance Python for Data Analytics by Tiago Rodrigues Antao
In this article we will start to explore Python’s framework for concurrency – the first step in developing parallel applications.
From Chaos Engineering by Mikolaj Pawlikowski
This article explores how you can apply Chaos engineering principles to make your team better.
From Operations Anti-Patterns, DevOps Solutions by Jeffery Smith
This article covers
Longer release cycles and their impact to the team’s deployment confidence
Automation techniques for deployments
The value of code deployment artifacts
Feature flags for releasing incomplete code
From Unified Logging with Fluentd by Phil Wilkins
This article describes how and when to deploy Fluentd.
From Learn Kubernetes in a Month of Lunches by Elton Stoneman
This article delves into getting started running pods with controllers in Kubernetes.
From Practices of the Python Pro by Dane Hillard
This article covers
• Recognizing the signs of tightly coupled code
• Strategies for reducing coupling
From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis
Want a job in Data Science?
This article discusses one popular way to get the skills that you’re going to need: attending a bootcamp.
From Designing Cloud Data Platforms by Danil Zburivsky and Lynda Partner
In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges. It would also be limited in terms of the types of data consumers (people and systems who consume the data from the platform) it supports, as they’re also growing in both numbers and variety.
From Ensemble Methods for Machine Learning by Gautam Kunapuli
Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.