The linked post covers how Apache Kafka and its Streams API are used for storing and processing all the articles ever published by The New York Times Source: Publishing with Apache Kafka at The New York Times | Confluent
Have you noticed that something on the site does not work and more is new instead? That’s true! I’m trying to do something different and I’m in a creative phase. Stay tuned
How can you find out if you’re being successful or heading for a burnout? The only person who can really answer that question is you. A Q&A with Peter Stevens and Maria Matarelli who spoke about success or burnout and personal agility at eXperience Agile 2018. Source: Success or Burnout? Q&A on How Personal Agility Can Help
Introduction As a company that manages our own infrastructure we need to be able to rapidly install new server capacity and ensure that the equipment entering our production environment is highly reliable. Source: Automating Datacenter Operations at Dropbox
On QCon.ai Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production. The meeting is well structured and divided into 3 large parts: The pipeline and why do forecasting, what is the challenge they face The Model Management ( 17:52m ) Data Science and Engineerng Work Flow (34:40 pm) Source: Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace
Yes… the bash documentation that I’ve always looked for and that I’ve never found, I love this team! Declaring the intentions as: The main motivation was to provide human-readable documentation and information so users aren’t forced to read every bit of the Bash manpage – which can be difficult to understand. Source: The Bash Hackers Wiki [Bash Hackers Wiki] I found this site extremely useful and recommend it to everyone; however, the docs here are not meant as a newbie tutorial. The Bash Hackers Wiki [Bash Hackers Wiki]
The ecosystem of Big Data analysis has evolved in recent years with new databases, streaming data and machine learning solutions which require more than the classic deployment model. The revolution of Container technologies try to cover these new objectives and there are possible to accomplish in the organizations. Below 2 articles where you can start thinking about what is most suitable for your games: Building GPU Accelerated Workflows with TensorFlow and Kubernetes Daniel Whitenack spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about GPU based deep learning workflows using TensorFlow and Kubernetes technologies. He discussed the open...
Recommended reading: Sahil Dua explained how Booking.com has been able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at QCon London conference. In particular, he stressed how Kubernetes elasticity and resource starvation avoidance on containers helps them run computationally (and data) intensive, hard to parallelize, machine learning models. Source: How Booking.com Uses Kubernetes for Machine Learning
Learn about the ten machine learning algorithms that you should know in order to become a data scientist. Source: 10 Machine Learning Algorithms You Should Know to Become a Data Scientist – DZone AI
From the blog of Google Developers an very interesting article on how to run C code in a modern browser via Emscripten and WebAssembly Checkout it on Emscripting a C library to Wasm | Web | Google Developers Do you know what Emscripting is? just quote the official site Emscripten is an Open Source LLVM to JavaScript compiler. Using Emscripten you can: Compile C and C++ code into JavaScript Compile any other code that can be translated into LLVM bitcode into JavaScript. Compile the C/C++ runtimes of other languages into JavaScript, and then run code in those other languages...
