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    <title>Hamza Boughanim — Technical Blog</title>
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    <description>Expert articles on AI engineering, MLOps, LLMs, computer vision, and full-stack development by Hamza Boughanim.</description>
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    <managingEditor>hamzaboughanim06@gmail.com (Hamza Boughanim)</managingEditor>
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    <copyright>2026 Hamza Boughanim. All rights reserved.</copyright>
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      <title>MLOps in Practice: Deploying AI Models with Docker and FastAPI</title>
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      <pubDate>Sun, 05 Jul 2026 00:00:00 +0000</pubDate>
      <dc:creator>Hamza Boughanim</dc:creator>
      <category>MLOps</category>
      <category>Docker</category>
      <category>FastAPI</category>
      <category>CI/CD</category>
      <description><![CDATA[A complete production guide to containerizing and serving machine learning models — covering FastAPI model servers, multi-stage Docker builds, GPU support, health checks, Docker Compose orchestration, and a CI/CD pipeline with GitHub Actions.]]></description>
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      <title>Building a Production RAG Pipeline: From Document Ingestion to Sub-Second Retrieval</title>
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      <pubDate>Sat, 04 Jul 2026 00:00:00 +0000</pubDate>
      <dc:creator>Hamza Boughanim</dc:creator>
      <category>AI Engineering</category>
      <category>RAG</category>
      <category>LLM</category>
      <category>pgvector</category>
      <description><![CDATA[A complete walkthrough of a production-grade Retrieval-Augmented Generation system — document ingestion, chunking strategy, embedding models, pgvector indexing with HNSW, reranking, and a FastAPI serving layer.]]></description>
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      <title>Entropy in Decision Trees: From Information Theory to Overfitting Control</title>
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      <pubDate>Wed, 15 Apr 2026 00:00:00 +0000</pubDate>
      <dc:creator>Hamza Boughanim</dc:creator>
      <category>Machine Learning</category>
      <category>Entropy</category>
      <category>Decision Trees</category>
      <category>Overfitting</category>
      <description><![CDATA[An expert-level exploration of entropy in decision trees, explaining how uncertainty reduction drives learning, why greedy entropy minimization leads to overfitting, and how modern constraints restore generalization.]]></description>
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      <title>Building a Zero-Trust AI Backend with Argon2: Security by Design, Not by Chance</title>
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      <pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate>
      <dc:creator>Hamza Boughanim</dc:creator>
      <category>AI Engineering</category>
      <category>Zero-Trust</category>
      <category>Argon2</category>
      <category>FastAPI</category>
      <description><![CDATA[A behind-the-scenes tour of a production-grade AI API that marries GPU-resistant Argon2 passwords, Zero-Trust micro-segmentation, infra-level prompt firewalls and scoped 60-second JWTs — so attackers stealing everything still can't exceed 128 tokens.]]></description>
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