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

Presentation: Scaling to 100+ as a Director: Lessons From Growing Engineering Organizations

Thiago Ghisi discusses the strategic evolution required to lead 100+ engineers without breaking the organization. He explains his "Three Levels of Impact" framework and shares practical lessons on speeding up decision-making, cultivating leadership teams, and building organizational resilience. Learn why he views reorgs as a continuous deployment feature rather than a one-time traumatic event.

By Thiago Ghisi

Airbnb Expands Global Checkout with “Pay as a Local,” Scaling to 220 Markets in 14 Months

Airbnb expands its global checkout with the “Pay as a Local” initiative, supporting over 20 locally preferred payment methods across 220 markets. The company replatformed its payments system with domain-oriented services, reusable flow archetypes, and a centralized configuration, enhancing integration speed, reliability, testing, and observability for diverse payment methods worldwide.

By Leela Kumili

MyTerms: A New IEEE Standard Enabling Online Privacy and Aiming to Replace Cookies

Nicknamed MyTerms, the new 7012-2025 IEEE standard defines mechanisms for exchanging personal information between individuals and online service providers, and specifies how individuals can enforce their privacy requirements during transactions.

By Sergio De Simone

Daggr Introduced as an Open-Source Python Library for Inspectable AI Workflows

The Gradio team has released Daggr, a new open-source Python library designed to simplify the construction and debugging of multi-step AI workflows. Daggr allows developers to define workflows programmatically in Python while automatically generating a visual canvas that exposes intermediate states, inputs, and outputs for each step in the pipeline.

By Robert Krzaczyński

Article: Why Most Machine Learning Projects Fail to Reach Production

In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.

By Wenjie Zi

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