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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
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Google Cloud's introduction of fully managed Model Context Protocol (MCP) servers revolutionizes its API infrastructure, streamlining access for developers. This enterprise-ready solution enhances AI integration across services such as Google Maps and BigQuery while promoting wide-scale adoption. New tools ensure governance and security, and are currently in public preview.
By Steef-Jan Wiggers
While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change.
By Jonathan McPhail, Juan Medina, Jake DeCrane, Isuru Wijesundara
David Stein, Principal AI Engineer at ServiceTitan, presented “Moving Mountains: Migrating Legacy Code in Weeks instead of Years” at QCon AI New York 2025. Stein demonstrated how migrations don’t have to be synonymous to “moving mountains” and introduced the concepts of the Principle of Acceleration and the Assembly Line Pattern.
By Michael Redlich
At QCon AI NYC 2025, Will Hang from OpenAI unveiled Agent RFT—a cutting-edge reinforcement fine-tuning approach for tool-using agents. By optimizing prompts and tasks before model adjustments, Hang showcased effective strategies to enhance decision-making and efficiency, emphasizing a balanced grading system. The session revealed a future where smarter agents reduce latency and improve outcomes.
By Andrew Hoblitzell
Durai Arasan explains the architectural strategies used to scale Chase.com to 67M+ active users. He discusses achieving high resilience through multi-region isolation, slashing latency by 71% via edge computing, and utilizing automated "infrastructure repaving" to eliminate security drift. He shares vital lessons on self-healing observability and building an engineering-first culture.
By Durai Arasan
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