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    <title>Model Machines</title>
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    <description>Essays on production machine learning infrastructure: evaluation design, inference latency and cost, model and data versioning, drift detection, retraining triggers, feature pipelines and batching.</description>
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      <title>Why Offline Gains Vanish Online</title>
      <link>https://modelmachines.io/why-offline-gains-vanish-online/</link>
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      <pubDate>Wed, 29 Jul 2026 00:00:00 +0000</pubDate>
      <category>Evaluation</category>
      <description>The metric improved, the launch did nothing. There are a small number of mechanisms that cause this, and each of them can be checked before you spend a release cycle finding out.</description>
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      <title>Feature Stores Solve a Skew Problem</title>
      <link>https://modelmachines.io/feature-stores-solve-a-skew-problem/</link>
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      <pubDate>Sat, 18 Jul 2026 00:00:00 +0000</pubDate>
      <category>Data</category>
      <description>The case for a feature store is not storage or reuse. It is that training and serving compute the same feature twice, in different code, at different moments — and the two answers diverge.</description>
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      <title>Drift Is a Question, Not an Alarm</title>
      <link>https://modelmachines.io/drift-is-a-question-not-an-alarm/</link>
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      <pubDate>Fri, 10 Jul 2026 00:00:00 +0000</pubDate>
      <category>Monitoring</category>
      <description>Input distributions move constantly and most of the movement is harmless. Wiring a retraining trigger to a drift statistic buys expensive churn; tying it to outcomes buys something worth having.</description>
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      <title>Versioning the Model Means Versioning the Data</title>
      <link>https://modelmachines.io/versioning-the-model-means-versioning-the-data/</link>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <category>Reproducibility</category>
      <description>A model checkpoint is one member of a tuple. Storing it without the data, code and configuration that produced it gives you an artefact you can serve and cannot explain.</description>
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      <title>Batching Is a Scheduling Policy</title>
      <link>https://modelmachines.io/batching-is-a-scheduling-policy/</link>
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      <pubDate>Mon, 22 Jun 2026 00:00:00 +0000</pubDate>
      <category>Serving</category>
      <description>Grouping requests is usually treated as a throughput trick. It is really a scheduling decision that determines who waits, for how long, and what happens when memory runs out.</description>
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      <title>Latency and Cost Are the Same Dial</title>
      <link>https://modelmachines.io/latency-and-cost-are-the-same-dial/</link>
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      <pubDate>Sat, 13 Jun 2026 00:00:00 +0000</pubDate>
      <category>Serving</category>
      <description>Serving cheaply and serving fast are opposing settings of one control. Understanding why makes capacity planning tractable and stops teams chasing a target that only exists on an idle machine.</description>
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      <title>Evaluation That Survives Production</title>
      <link>https://modelmachines.io/evaluation-that-survives-production/</link>
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      <pubDate>Thu, 04 Jun 2026 00:00:00 +0000</pubDate>
      <category>Evaluation</category>
      <description>An evaluation suite earns its keep by failing when the product would fail. Most suites fail somewhere else entirely — on a frozen set, against an average that hides the cases anyone would complain about.</description>
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