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Posted on August 4, 2015 at 11:15am 0 Comments 1 Like
This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.
https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html
Posted on April 13, 2015 at 9:14am 1 Comment 0 Likes
Recommendation engines help narrow your choices to those that best meet your particular needs. In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…
ContinuePosted on March 30, 2009 at 10:30am 0 Comments 0 Likes
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Chronon, Airbnb platform to create the infrastructure required to transform raw data into ML-ready features, is now open source. As Airbnb ML infrastructure engineer Varant Zanoyan explains, Chronon supports a variety of data sources and aims to provide low-latency streaming.
By Sergio De SimoneSoftware sustainability includes computing for environmental purposes and using resources appropriately. According to Coral Calero, software engineers need a holistic way of looking at software and should be aware of the environmental impact of software. Several tools and frameworks are available for software engineers to do sustainable software development.
By Ben LindersClint Dovholuk reviews the three components of OpenZiti's architecture: controller, edge routers, and SDKs in addition to diving into the internal physical and logical architecture of OpenZiti.
By Clint DovholukJoni Collinge, Diagrid's Founding Software Engineer, presented at QCon London and discussed a case study on the evolutionary design and implementation of the Diagrid Cloud platform, which underpins Diagrid’s SaaS offerings.
By Steef-Jan WiggersDuring KubeCon EU the Mercedes Benz team presented their migration journey from Pod Security Policies to Validation Admission Policies to secure their 1000+ Kubernetes clusters. The solution was chosen in favour of Kyverno due to its improved performance.
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