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        <title>MachineLearningMastery.com</title>
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        <description>Making developers awesome at machine learning</description>
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                	<title>Retrieval vs. Memory in Agentic AI Systems</title>
               		<description><![CDATA[In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively....]]></description>
                	<link>https://machinelearningmastery.com/retrieval-vs-memory-in-agentic-ai-systems/</link>
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                	<pubDate>Wed, 12 Aug 2026 12:00:35 +0000</pubDate>
                	<dc:creator><![CDATA[Bala Priya C]]></dc:creator>
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                	<title>7 Async Patterns for Running Agents Concurrently in Python</title>
               		<description><![CDATA[In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production-level...]]></description>
                	<link>https://machinelearningmastery.com/7-async-patterns-for-running-agents-concurrently-in-python/</link>
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                	<pubDate>Tue, 11 Aug 2026 12:00:35 +0000</pubDate>
                	<dc:creator><![CDATA[Vinod Chugani]]></dc:creator>
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                	<title>Prompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework</title>
               		<description><![CDATA[In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how...]]></description>
                	<link>https://machinelearningmastery.com/prompt-caching-vs-fine-tuning-a-cost-and-latency-decision-framework/</link>
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                	<pubDate>Mon, 10 Aug 2026 12:00:54 +0000</pubDate>
                	<dc:creator><![CDATA[Iván Palomares Carrascosa]]></dc:creator>
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                	<title>Identifying Token Costs Hiding in Your Agentic Loop</title>
               		<description><![CDATA[But cutting your runtime token burn is just the first problem.]]></description>
                	<link>https://machinelearningmastery.com/identifying-token-costs-hiding-in-your-agentic-loop/</link>
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                	<pubDate>Fri, 07 Aug 2026 13:19:16 +0000</pubDate>
                	<dc:creator><![CDATA[Vinod Chugani]]></dc:creator>
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                	<title>Designing AI Agents That Can Self-Correct</title>
               		<description><![CDATA[With the vocabulary and the failure modes in place, here's the build.]]></description>
                	<link>https://machinelearningmastery.com/designing-ai-agents-that-can-self-correct/</link>
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                	<pubDate>Thu, 06 Aug 2026 11:13:29 +0000</pubDate>
                	<dc:creator><![CDATA[Shittu Olumide]]></dc:creator>
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                	<title>7 Chunking Strategies That Decide Whether Your RAG Works</title>
               		<description><![CDATA[Day 100 in production isn't really about chunking strategies anymore.]]></description>
                	<link>https://machinelearningmastery.com/7-chunking-strategies-that-decide-whether-your-rag-works/</link>
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                	<pubDate>Wed, 05 Aug 2026 12:00:38 +0000</pubDate>
                	<dc:creator><![CDATA[Vinod Chugani]]></dc:creator>
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                	<title>Measuring Performance of Transformer Inference</title>
               		<description><![CDATA[This chapter is divided into eight parts; they are: • Metrics for LLM Inference • Measuring a Single Request • Warmup and Synchronization • Measuring GPU Work with CUDA Events • Measuring Memory Usage • Measuring Concurrent Requests • Multiple GPUs and Multiple Machines • Cost per Token The most common inference metrics are: • Latency:  How long a request takes from start to finish.]]></description>
                	<link>https://machinelearningmastery.com/measuring-performance-of-transformer-inference/</link>
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                	<pubDate>Tue, 04 Aug 2026 14:00:56 +0000</pubDate>
                	<dc:creator><![CDATA[Adrian Tam]]></dc:creator>
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                	<title>Static vs. Dynamic vs. Continuous Batching in LLM Inference</title>
               		<description><![CDATA[In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production...]]></description>
                	<link>https://machinelearningmastery.com/static-vs-dynamic-vs-continuous-batching-in-llm-inference/</link>
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                	<pubDate>Tue, 04 Aug 2026 12:00:10 +0000</pubDate>
                	<dc:creator><![CDATA[Bala Priya C]]></dc:creator>
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                	<title>Decoding Strategies and Output Control</title>
               		<description><![CDATA[This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every position in the input sequence.]]></description>
                	<link>https://machinelearningmastery.com/decoding-strategies-and-output-control/</link>
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                	<pubDate>Mon, 03 Aug 2026 14:36:17 +0000</pubDate>
                	<dc:creator><![CDATA[Adrian Tam]]></dc:creator>
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                	<title>Using a Transformer Model: From Training to Inference</title>
               		<description><![CDATA[This chapter is divided into four parts; they are: • Autoregressive Generation • Prefill and Decode • A Simple KV Cache • Memory Usage of the KV Cache A decoder-only transformer model predicts the next token from the tokens that came before it.]]></description>
                	<link>https://machinelearningmastery.com/using-a-transformer-model-from-training-to-inference/</link>
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                	<pubDate>Fri, 31 Jul 2026 14:22:34 +0000</pubDate>
                	<dc:creator><![CDATA[Adrian Tam]]></dc:creator>
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