From Distributed Machine Learning Patterns by Yuan Tang
In this article, we introduce the collective communication pattern, which is a great alternative to parameter servers when the machine learning model we are building is not too large without having to tune the ratio between the number of workers and parameter servers.
From Distributed Machine Learning Patterns by Yuan Tang
In this article, we introduce the parameter server pattern which comes handy for situations where the model is too large to fit in a single machine such as one we would have to build for tagging entities in the 8 millions of YouTube videos.
In case you missed it, here is Jeff Nickoloff and Stephen Kuenzli’s live Twitch coding stream recap. For more, check out the book: Docker in Action, Second Edition. For more live coding streams, subscribe to Manning’s Twitch channel here: https://www.twitch.tv/manningpublications
From Azure Infrastructure as Code by Henry Been, Eduard Keilholz, and Erwin Staal
This is an excerpt from chapter 6, which starts with introducing the Bicep language, a new and improved IaC language for a Azure. This snippet is the next section of the chapter, which lists the benefits of Bicep over ARM Templates.
Let’s jump into using Bicep templates to improve ARM template syntax!
From Software Telemetry by Jamie Riedesel
This article covers emitting and shipping telemetry to somewhere off the system, such as into a queue.
In this video, Billy, Alexander, Todd, and Jesse explain key concepts and walk you through demos of GitOps and Kubernetes.
Co-author of Docker in Action, Second Edition. and the designer of scalable software systems Stephen Kuenzli explains what are Docker containers and showcases how to run them securely.
From BDD in Action, Second Edition by John Ferguson Smart
This article explores BDD automation tools that are available for use.
From Operations Anti-Patterns, DevOps Solutions by Jeffery D. Smith
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