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                	<title>Python Concepts Every AI Engineer Must Master</title>
               		<description><![CDATA[Transitioning from writing local experimental scripts to building scalable, production-grade AI systems requires a shift in how we write Python.]]></description>
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                	<pubDate>Fri, 12 Jun 2026 12:00:39 +0000</pubDate>
                	<dc:creator><![CDATA[Matthew Mayo]]></dc:creator>
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                	<title>Multi-Label Text Classification with Scikit-LLM</title>
               		<description><![CDATA[Text classification typically boils down to scenarios where a product review is "positive" or "negative", or a customer inquiry belongs to one category or another.]]></description>
                	<link>https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/</link>
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                	<pubDate>Thu, 11 Jun 2026 12:00:17 +0000</pubDate>
                	<dc:creator><![CDATA[Iván Palomares Carrascosa]]></dc:creator>
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                	<title>Multimodal Browser AI with Transformers.js for Images and Speech</title>
               		<description><![CDATA[Most browser AI tutorials cover text because it is a natural starting point, but the applications people actually want to build are rarely text-only.]]></description>
                	<link>https://machinelearningmastery.com/multimodal-browser-ai-with-transformers-js-for-images-and-speech/</link>
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                	<pubDate>Wed, 10 Jun 2026 11:35:14 +0000</pubDate>
                	<dc:creator><![CDATA[Shittu Olumide]]></dc:creator>
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                	<title>The Practitioner&#8217;s Guide to AgentOps</title>
               		<description><![CDATA[According to Futurum Research's 2025 market overview of agentic AI platforms, <a href="https://zbrain.]]></description>
                	<link>https://machinelearningmastery.com/the-practitioners-guide-to-agentops/</link>
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                	<pubDate>Mon, 08 Jun 2026 15:21:11 +0000</pubDate>
                	<dc:creator><![CDATA[Shittu Olumide]]></dc:creator>
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                	<title>Building Semantic Search with Transformers.js and Sentence Embeddings</title>
               		<description><![CDATA[You've probably shipped this bug before, where a user types " affordable laptop " into your search bar and gets zero results.]]></description>
                	<link>https://machinelearningmastery.com/building-semantic-search-with-transformers-js-and-sentence-embeddings/</link>
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                	<pubDate>Fri, 05 Jun 2026 12:00:01 +0000</pubDate>
                	<dc:creator><![CDATA[Shittu Olumide]]></dc:creator>
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                	<title>Using Scikit-LLM with Open-Source LLMs</title>
               		<description><![CDATA[This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama &mdash; a free repository for local LLMs &mdash; and the Scikit-LLM Python library.]]></description>
                	<link>https://machinelearningmastery.com/using-scikit-llm-with-open-source-llms/</link>
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                	<pubDate>Thu, 04 Jun 2026 12:55:34 +0000</pubDate>
                	<dc:creator><![CDATA[Iván Palomares Carrascosa]]></dc:creator>
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                	<title>Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?</title>
               		<description><![CDATA[In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .]]></description>
                	<link>https://machinelearningmastery.com/scikit-llm-vs-traditional-text-classifiers-when-should-you-use-an-llm/</link>
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                	<pubDate>Tue, 02 Jun 2026 12:00:18 +0000</pubDate>
                	<dc:creator><![CDATA[Iván Palomares Carrascosa]]></dc:creator>
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                	<title>The Roadmap for Mastering LLMOps in 2026</title>
               		<description><![CDATA[The LLMOps market is projected to grow from <a href="https://www.]]></description>
                	<link>https://machinelearningmastery.com/the-roadmap-for-mastering-llmops-in-2026/</link>
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                	<pubDate>Mon, 01 Jun 2026 12:00:18 +0000</pubDate>
                	<dc:creator><![CDATA[Shittu Olumide]]></dc:creator>
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                	<title>Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient</title>
               		<description><![CDATA[This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.]]></description>
                	<link>https://machinelearningmastery.com/serving-multiple-users-at-once-how-continuous-batching-keeps-llm-inference-efficient/</link>
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                	<pubDate>Sat, 30 May 2026 02:54:17 +0000</pubDate>
                	<dc:creator><![CDATA[Yoyo Chan]]></dc:creator>
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                	<title>Building a Context Pruning Pipeline for Long-Running Agents</title>
               		<description><![CDATA[Modern AI agents built on top of large language models (LLMs) are designed to run continuously.]]></description>
                	<link>https://machinelearningmastery.com/building-a-context-pruning-pipeline-for-long-running-agents/</link>
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                	<pubDate>Thu, 28 May 2026 12:00:39 +0000</pubDate>
                	<dc:creator><![CDATA[Iván Palomares Carrascosa]]></dc:creator>
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