Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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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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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

Google Launches Automated Review Feature in Gemini CLI Conductor

Google has enhanced its Gemini CLI extension, Conductor, by adding support for automated reviews. The company says this update allows Conductor "to go beyond just planning and execution into validation", enabling it to check AI-generated code for quality and adherence to guidelines, strengthening confidence, safety, and control in AI-assisted development workflows.

By Sergio De Simone

From Central Control to Team Autonomy: Rethinking Infrastructure Delivery

Adidas engineers describe shifting from a centralized Infrastructure-as-Code model to a decentralized one. Five teams autonomously deployed over 81 new infrastructure stacks in two months, using layered IaC modules, automated pipelines, and shared frameworks. The redesign illustrates how to scale infrastructure delivery while maintaining governance at scale.

By Leela Kumili

Google Publishes Scaling Principles for Agentic Architectures

Researchers from Google and MIT published a paper describing a predictive framework for scaling multi-agent systems. The framework shows that there is a tool-coordination trade-off and it can be used to select an optimal agentic architecture for a given task.

By Anthony Alford

Google Cloud Brings Full OpenTelemetry Support to Cloud Monitoring Metrics

Google Cloud recently unveiled broad support for the OpenTelemetry Protocol (OTLP) in Cloud Monitoring, marking a step toward unifying telemetry collection across its observability stack.

By Craig Risi

AWS Launches Agent Plugins to Automate Cloud Deployment

AWS launched Agent Plugins for AWS, providing AI coding agents with specialized deployment skills. The initial deploy-on-aws plugin transforms workflows by accepting commands like "deploy to AWS" and generating complete pipelines with architecture recommendations, cost estimates, and infrastructure code. Supported in Claude Code and Cursor, AWS claims 10-minute deployments versus hours manually.

By Steef-Jan Wiggers

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