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Carol McDonald
- Female
- Jacksonville, Florida
- United States
- Blog Posts (3)
- Discussions (4)
- Events
- Groups (9)
- Photos
- Photo Albums
- Videos
Carol McDonald's Groups
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Community Corral
23 members
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iPhone Development
40 members
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Database SIG
13 members
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JEE Town
28 members
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Spring Town
47 members
Carol McDonald's Discussions
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
Gifts Received
Carol McDonald has not received any gifts yet
Carol McDonald's Page
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 1 Like
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 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,…
Continuescreencast about MySQL for Developers
Posted on March 30, 2009 at 10:30am 0 Comments 0 Likes
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
Happy 10th year, JCertif!
Notes
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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
Article: NextGen Search - Where AI Meets OpenSearch Through MCP
In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP).
By Srikanth Daggumalli, Arun LakshmananNetflix Migrates to Amazon Aurora: 75% Performance Boost and 28% Cost Reduction
Netflix consolidated its relational databases onto Amazon Aurora, cutting costs by 28% and boosting performance by up to 75%. The move from self-managed PostgreSQL reduced operational toil, improving latency for critical apps. This mirrors migrations by Samsung and Panasonic, though benchmarks suggest alternatives like Timescale may suit specific workloads better.
By Mark SilvesterTornadoVM 2.0 Brings Automatic GPU Acceleration and LLM support to Java
The TornadoVM project recently reached version 2.0, a major milestone for the open-source project that aims to provide a heterogeneous hardware runtime for Java. The project automatically accelerates Java programs on multi-core CPUs, GPUs, and FPGAs. This release is likely to be of particular interest to teams developing LLM solutions on the JVM.
By Ben EvansTransformers v5 Introduces a More Modular and Interoperable Core
Hugging Face has released the first candidate for Transformers v5, marking a significant evolution from v4 five years ago. The library has grown from a specialized model toolkit to a critical resource in AI development, achieving over three million installations daily and more than 1.2 billion total installs.
By Robert KrzaczyńskiMeta's Optimization Platform Ax 1.0 Streamlines LLM and System Optimization
Now stable, Ax is an open-source platform from Meta designed to help researchers and engineers apply machine learning to complex, resource-intensive experimentation. Over the past several years, Meta has used Ax to improve AI models, accelerate machine learning research, tune production infrastructure, and more.
By Sergio De Simone
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