|Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning
Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components.
From Making Sense of Edge Computing by Cody Bumgardner
Conceptually, edge computing is concerned with when it’s best to migrate computational functionally toward source of data and when it is best to move the data itself. This abstract concept of function versus data migration drives not only the fundamental motivations of edge computing, but also the broader field of distributed systems. The act of distributing processes makes even the simplest tasks more complicated.
From AWS Security by Dylan Shields
This article deals with methods you can use to secure your AWS account.
From React Hooks in Action by John Larsen
If you’re building React apps, then you’re expecting the data your app uses to change over time. Whether it’s fully server-rendered, a mobile app or all in a browser, your application’s user interface should represent the current data or state at the time of rendering. Sometimes multiple components throughout the app will use the data, and sometimes a component doesn’t need to share its secrets and can manage its own state without the help of mammoth, application-wide state-store behemoths. In this article, we’ll keep it personal and concentrate on components taking care of themselves, without regard for other components around them.