April 2015 Blog Posts (2)

JavaOne

Folks,



Perhaps this is old news for you, but JavaOne San Francisco registration is live. Various saving options are available leading up to the conference, and to take advantage of the current US$600 in savings registration needs to be completed by May 31st, 2015 (11:59pm PT).



I encourage you to read through the registration options by visiting the JavaOne registration site:…

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Added by Michael Levin on April 24, 2015 at 11:00am — No Comments

An Inside Look at the Components of a Recommendation Engine

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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Added by Carol McDonald on April 13, 2015 at 9:14am — 1 Comment

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Notes

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When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
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InfoQ Reading List

Transformers v5 Introduces a More Modular and Interoperable Core

Hugging Face has released the first candidate for Transformers v5, marking a significant evolution from v4 five years ago. The library has grown from a specialized model toolkit to a critical resource in AI development, achieving over three million installations daily and more than 1.2 billion total installs.

By Robert Krzaczyński

Meta's Optimization Platform Ax 1.0 Streamlines LLM and System Optimization

Now stable, Ax is an open-source platform from Meta designed to help researchers and engineers apply machine learning to complex, resource-intensive experimentation. Over the past several years, Meta has used Ax to improve AI models, accelerate machine learning research, tune production infrastructure, and more.

By Sergio De Simone

Lyft Rearchitects ML Platform with Hybrid AWS SageMaker-Kubernetes Approach

Lyft has rearchitected its machine learning platform LyftLearn into a hybrid system, moving offline workloads to AWS SageMaker while retaining Kubernetes for online model serving. Its decision to choose managed services where operational complexity was highest, while maintaining custom infrastructure where control mattered most, offers a pragmatic alternative to unified platform strategies.

By Eran Stiller

AWS Transform Custom Tackles Technical Debt

AWS Transform Custom revolutionizes code modernization with AI-driven, out-of-the-box transformations for Java, Node.js, and Python. This enterprise-focused tool accelerates application upgrades by up to 5x while learning from organizational nuances to deliver high-quality, repeatable transformations.

By Steef-Jan Wiggers

Article: Trustworthy Productivity: Securing AI Accelerated Development

Autonomous AI agents amplify productivity but can cause severe damage without safeguards. Defend the ReAct loop—context, reasoning, and tools—through provenance gates, planner-critic separation, scoped credentials, sandboxed code, and STRIDE/MAESTRO threat modeling. With robust logging, bounded autonomy, and red-teaming, agents can deliver trustworthy productivity while minimizing risk.

By Sriram Madapusi Vasudevan

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