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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
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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
pnpm 11 Release Candidate: ESM Distribution, Supply Chain Defaults and a New Store Format
pnpm 11 RC has been released, featuring significant changes in performance, security, and configuration. Key updates include an SQLite-backed store index, tighter security defaults, and a consolidated build script setting. It now requires Node.js v22 or later. Global installs are isolated by default, and new commands enhance usability. Migration guidance is available in the documentation.
By Daniel CurtisAnthropic Introduces Managed Agents to Simplify AI Agent Deployment
Anthropic introduces Managed Agents on Claude, a managed execution layer for agent-based workflows. It separates agent logic from runtime concerns like orchestration, sandboxing, state management, and credentials. The system supports long-running multi-step workflows with external tools, error recovery, and session continuity via a meta-harness architecture.
By Leela KumiliSlack Rebuilds Notification System, Reports 5X Increase in Settings Engagement
Slack has rebuilt its notification system with a unified architecture that separates activity from delivery, improving consistency across platforms. The redesign simplifies preferences, preserves legacy settings through transformation, and resulted in a 5x increase in user engagement with notification settings along with reduced support tickets.
By Leela KumiliGitHub Acknowledges Recent Outages, Cites Scaling Challenges and Architectural Weaknesses
GitHub has publicly addressed a series of recent availability and performance issues that disrupted services across its platform, attributing the incidents to rapid growth, architectural coupling, and limitations in handling system load.
By Craig RisiPresentation: Dynamic Moments: Weaving LLMs into Deep Personalization at DoorDash
Sudeep Das and Pradeep Muthukrishnan explain the shift from static merchandising to dynamic, moment-aware personalization at DoorDash. They share how LLMs generate natural-language "consumer profiles" and content blueprints, while traditional deep learning handles last-mile ranking. This hybrid approach allows the platform to adapt to short-lived user intent and massive catalog abundance.
By Sudeep Das, Pradeep Muthukrishnan
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