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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
Carol McDonald has not received any gifts yet
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
Article: Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs
Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.
By Saranya VedagiriUber Moves from Static Limits to Priority-Aware Load Control for Distributed Storage
Uber engineers detailed how they evolved their storage platform from static rate limiting to a priority-aware load management system. The approach protects Docstore and Schemaless, Uber’s MySQL-based distributed databases, by colocating control with storage, prioritizing critical traffic, and dynamically shedding load under overload conditions.
By Leela KumiliBuilding Software Organisations Where People Can Thrive
Continuous learning, adaptability, and strong support networks are the foundations for thriving teams, Matthew Card mentioned. Trust is built through consistent, fair leadership and addressing toxic behaviour, bias, and microaggressions early. By fostering growth, psychological safety, and accountability, people-first leadership drives resilience, collaboration, and performance.
By Ben LindersGoogle DeepMind Introduces ATLAS Scaling Laws for Multilingual Language Models
Google DeepMind researchers have introduced ATLAS, a set of scaling laws for multilingual language models that formalize how model size, training data volume, and language mixtures interact as the number of supported languages increases.
By Robert KrzaczyńskiPresentation: Foundation Models for Ranking: Challenges, Successes, and Lessons Learned
Moumita Bhattacharya discusses the evolution of Netflix’s ranking systems, from the multi-model architecture to a Unified Contextual Recommender (UniCoRn). She explains how they built a task-agnostic User Foundation Model to capture long-term member preferences. Learn how they solve system challenges like high-throughput inference and the tradeoff between relevance and personalization.
By Moumita Bhattacharya
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