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

Uforwarder: Uber’s Scalable Kafka Consumer Proxy for Efficient Event-Driven Microservices

Uber has open-sourced uForwarder, a push-based Kafka consumer proxy built to handle trillions of messages and multiple petabytes of data daily. The system introduces context-aware routing, head-of-line blocking mitigation, adaptive auto-rebalancing, and partition-level delay processing to improve scalability, workload isolation, and hardware efficiency in large-scale event-driven microservices.

By Leela Kumili

TSSLint 3.0: Final Major Release with Reduced Dependencies

TSSLint 3, the lightweight TypeScript linting tool by Johnson Chu, enhances performance with a reduced dependencies and improved migration paths from legacy linters. As a spiritual successor to TSLint, it offers near-instant diagnostics and fixes, leveraging native Node support for .ts imports. Enhanced developer tooling and a new TSL compatibility layer simplify linting in large-scale projects.

By Daniel Curtis

Article: Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners

This article presents a least-privilege AI Agent Gateway that places clear controls between AI agents and infrastructure. Agents do not access infrastructure APIs directly. Instead, every request is validated, authorized using policy as code with Open Policy Agent (OPA), and executed in short-lived, isolated environments, with built-in observability using OpenTelemetry.

By Nabin Debnath

Podcast: Software Evolution with Microservices and LLMs: A Conversation with Chris Richardson

In this podcast, Michael Stiefel spoke with Chris Richardson about using microservices to modernize software applications and the use of artificial intelligence in software architecture. We first discussed the problems of monolithic enterprise software and how to use microservices to evolve them to enable fast flow - the ability to achieve rapid software delivery.

By Chris Richardson

Anthropic Study: AI Coding Assistance Reduces Developer Skill Mastery by 17%

Anthropic research shows developers using AI assistance scored 17% lower on comprehension tests when learning new coding libraries, though productivity gains were not statistically significant. Those who used AI for conceptual inquiry scored 65% or higher, while those delegating code generation to AI scored below 40%.

By Steef-Jan Wiggers

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