Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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Some Java Concurrency Tips

Started Sep 18, 2009

Java Enterprise Tech Tips: Locking and Concurrency in Java Persistence 2.0

Started Sep 14, 2009

Comparing Tomcat and Glassfish

Started Mar 6, 2009

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Profile Information

What are your main interests in software development?
Java, Web, Ajax, Java EE, Spring, Seam, JPA, Web Services
Do you have a website?
http://weblogs.java.net/blog/caroljmcdonald/
Anything else you'd like to add? Where do you live? (optional!)
Je parle Francais. Ich kann Deutsch.

Carol McDonald's Blog

movie recommendations with Spark machine learning

Posted on August 4, 2015 at 11:15am 0 Comments

Tutorial on Apache Spark, movie recommendations with machine learning 

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

An Inside Look at the Components of a Recommendation Engine

Posted on April 13, 2015 at 9:14am 1 Comment

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,…

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screencast about MySQL for Developers

Posted on March 30, 2009 at 10:30am 0 Comments

Here is a screencast about MySQL for Developers



If you are a developer using MySQL, you should learn enough to take advantage of its strengths, because having an understanding of the database can help you develop better-performing applications. This session will talk about MySQL database design and SQL tuning for developers. Some topics include:



* MySQL Storage Engine Architecture

* Schema, the basic foundation of performance

* Think about performance when… Continue

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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.

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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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InfoQ Reading List

Presentation: Foundation Models for Ranking: Challenges, Successes, and Lessons Learned

Moumita Bhattacharya discusses the evolution of Netflix’s ranking systems, from the multi-model architecture to a Unified Contextual Recommender (UniCoRn). She explains how they built a task-agnostic User Foundation Model to capture long-term member preferences. Learn how they solve system challenges like high-throughput inference and the tradeoff between relevance and personalization.

By Moumita Bhattacharya

Swift Cross-Platform Framework Skip Now Fully Open Source

After three years of development, the team behind Skip, a solution designed to create iOS and Android apps from a single Swift/SwiftUI codebase, has announced their decision to make the product completely and open source, in order to foster adoption and community contribution.

By Sergio De Simone

Railway Highlights the Importance of Logs, Metrics, Traces, and Alerts for Diagnosing System Failure

Railway’s engineering team published a comprehensive guide to observability, explaining how developers and SRE teams can use logs, metrics, traces, and alerts together to understand and diagnose production system failures.

By Craig Risi

Google BigQuery Adds SQL-Native Managed Inference for Hugging Face Models

Google has launched SQL-native managed inference for 180,000+ Hugging Face models in BigQuery. The preview release collapses the ML lifecycle into a unified SQL interface, eliminating the need for separate Kubernetes or Vertex AI management. Key features include automated resource governance via endpoint_idle_ttl and secure identity-based execution using existing data warehouse permissions.

By Steef-Jan Wiggers

Google Introduces TranslateGemma Open Models for Multilingual Translation

Google has released TranslateGemma, a set of open translation models based on the Gemma 3 architecture, offering 4B, 12B, and 27B parameter variants designed to support machine translation across 55 languages and to run on platforms ranging from mobile and edge devices to consumer hardware and cloud accelerators.

By Daniel Dominguez

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