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: The Time is Now: Delight Your Developers with User-Centric Platforms & Practices

Ana Petkovska discusses creating platform teams, establishing the team API, engagement of early adopters, easing adoption and providing a high quality product.

By Ana Petkovska

DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM

DeepSeek open-sourced DeepSeek-V3, a Mixture-of-Experts (MoE) LLM containing 671B parameters. It was pre-trained on 14.8T tokens using 2.788M GPU hours and outperforms other open-source models on a range of LLM benchmarks, including MMLU, MMLU-Pro, and GPQA.

By Anthony Alford

Article: A Framework for Building Micro Metrics for LLM System Evaluation

LLM accuracy is a challenging topic to address and is much more multi-dimensional than a simple accuracy score. Denys Linkov introduces a framework for creating micro metrics to evaluate LLM systems, focusing on goal-aligned metrics that improve performance and reliability. By adopting an iterative "crawl, walk, run" methodology, teams can incrementally develop observability.

By Denys Linkov

Google Releases Experimental AI Reasoning Model

Google has introduced Gemini 2.0 Flash Thinking Experimental, an AI reasoning model available in its AI Studio platform.

By Daniel Dominguez

Google Vertex AI Provides RAG Engine for Large Language Model Grounding

Vertex AI RAG Engine is a managed orchestration service aimed to make it easier to connect large language models (LLMs) to external data sources to be more up-to-date, generate more relevant responses, and hallucinate less.

By Sergio De Simone

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