Codetown
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April 2015 Blog Posts (2)
JavaOne
Folks,
Perhaps this is old news for you, but JavaOne San Francisco registration is live. Various saving options are available leading up to the conference, and to take advantage of the current US$600 in savings registration needs to be completed by May 31st, 2015 (11:59pm PT).
I encourage you to read through the registration options by visiting the JavaOne registration site:…
Added by Michael Levin on April 24, 2015 at 11:00am — No Comments
An Inside Look at the Components of a Recommendation Engine
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,…
ContinueAdded by Carol McDonald on April 13, 2015 at 9:14am — 1 Comment
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Notes
Welcome to Codetown!
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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InfoQ Reading List
Grab Adds Real-Time Data Quality Monitoring to Its Platform
Grab updated its internal platform to monitor Apache Kafka data quality in real time. The system uses FlinkSQL and an LLM to detect syntactic and semantic errors. It currently tracks 100+ topics, preventing invalid data from reaching downstream users. This proactive strategy aligns with industry trends to treat data streams as reliable products.
By Patrick FarryNVIDIA Dynamo Addresses Multi-Node LLM Inference Challenges
Serving Large Language Models (LLMs) at scale is complex. Modern LLMs now exceed the memory and compute capacity of a single GPU or even a single multi-GPU node. As a result, inference workloads for 70B+, 120B+ parameter models, or pipelines with large context windows, require multi-node, distributed GPU deployments.
By Claudio MasoloKarrot Improves Conversion Rates by 70% with New Scalable Feature Platform on AWS
Karrot replaced its legacy recommendation system with a scalable architecture that leverages various AWS services. The company sought to address challenges related to tight coupling, limited scalability, and poor reliability in its previous solution, opting instead for a distributed, event-driven architecture built on top of scalable cloud services.
By Rafal GancarzGrowing Yourself as a Software Engineer, Using AI to Develop Software
Sharing your work as a software engineer inspires others, invites feedback, and fosters personal growth, Suhail Patel said at QCon London. Normalizing and owning incidents builds trust, and it supports understanding the complexities. AI enables automation but needs proper guidance, context, and security guardrails.
By Ben LindersArticle: Scaling Cloud and Distributed Applications: Lessons and Strategies
The article shares goals and strategies for scaling cloud and distributed applications, focusing on lessons learned from cloud migration at Chase.com at JP Morgan Chase. The discussion centers on three primary goals and the strategies addressing the goals, concluding how these approaches were achieved in practice. For those managing large-scale systems, these lessons provide valuable guidance!
By Durai Arasan
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