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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Blog posts by Monzurul Amin - Thoughts on stream processing, energy profiling, and machine learning">
<title>Monzurul Amin - Blog</title>
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</head>
<body>
<div class="nav-container">
<nav>
<div class="nav-name">Monzurul Amin</div>
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<span></span>
<span></span>
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<a href="index.html#about">About</a>
<a href="index.html#research">Research</a>
<a href="index.html#publications">Publications</a>
<a href="index.html#education">Education</a>
<a href="index.html#experience">Experience</a>
<a href="index.html#projects">Projects</a>
<a href="index.html#skills">Skills</a>
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<main class="container">
<section id="blog">
<h1>Blog Posts</h1>
<div class="blog-grid">
<article class="blog-post">
<div class="blog-post-image">
<img src="assets/images/greenstream-theme.webp" alt="LLM Integration">
</div>
<div class="blog-post-content">
<span class="blog-post-date">November 10, 2024</span>
<h2>Sustainability Meets AI: GreenStream Optimizes Energy for LLMs</h2>
<p class="blog-post-excerpt">
Large Language Models (LLMs) are incredible—they power tools like fraud detectors, personalized recommendations, and even GenAI travel assistants. But let’s be real: these AI models come with a hefty energy bill. Enter GreenStream, a framework designed to tackle this challenge by making LLM inference in stream processing applications (SPAs) more sustainable. Here’s what you need to know.
</p>
<a href="posts\greenstream-intro.html" class="read-more">Read More</a>
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</article>
<article class="blog-post">
<div class="blog-post-image">
<img src="assets/images/scenario-overview_page-0001.jpg">
</div>
<div class="blog-post-content">
<span class="blog-post-date">April 17, 2024</span>
<h2>Are Data Streaming Platforms Ready for Mission-Critical Challenges?</h2>
<p class="blog-post-excerpt">
Ever wonder how technologies like Apache Kafka or RabbitMQ perform when the stakes are high—like in military coordination or disaster response? It’s not just about handling large amounts of data anymore; it’s about reliability under extreme conditions. A recent paper dives deep into this topic, analyzing whether today’s data streaming platforms can truly meet the demands of mission-critical applications. Here’s a quick glimpse.
</p>
<a href="posts\amnis-intro.html" class="read-more">Read More</a>
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</article>
<article class="blog-post">
<div class="blog-post-image">
<img src="assets/images/stream2gym-overview-image.webp" alt="Stream Processing">
</div>
<div class="blog-post-content">
<span class="blog-post-date">July 20, 2023</span>
<h2>Prototyping Stream Processing Made Simple with stream2gym</h2>
<p class="blog-post-excerpt">
An introduction to stream processing systems, their applications in real-time data analysis and prototyping using stream2gym.
</p>
<a href="posts\stream2gym-intro.html" class="read-more">Read More</a>
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