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    <title>InfoQ - Machine Learning</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Machine Learning feed</description>
    <item>
      <title>Netflix Introduces ‘Model Lifecycle Graph’ to Scale Enterprise Machine Learning</title>
      <link>https://www.infoq.com/news/2026/05/netflix-ml-graph/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Machine+Learning</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/netflix-ml-graph/en/headerimage/generatedHeaderImage-1778283879608.jpg"/&gt;&lt;p&gt;Netflix has developed a graph-based architecture for managing machine learning systems, called the Model Lifecycle Graph. This system maps interconnections between datasets, models, features, and workflows, addressing challenges in scaling ML operations. It enhances discoverability, governance, and component reuse while supporting a self-service approach for engineers and data scientists.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>MLOps</category>
      <category>AI Architecture</category>
      <category>Platform Engineering</category>
      <category>Machine Learning</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 11 May 2026 07:30:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/netflix-ml-graph/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Machine+Learning</guid>
      <dc:creator>Matt Foster</dc:creator>
      <dc:date>2026-05-11T07:30:00Z</dc:date>
      <dc:identifier>/news/2026/05/netflix-ml-graph/en</dc:identifier>
    </item>
    <item>
      <title>Confluent Moves Schema IDs to Kafka Headers to Simplify Schema Governance</title>
      <link>https://www.infoq.com/news/2026/05/confluent-kafka-header-schema-id/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Machine+Learning</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/confluent-kafka-header-schema-id/en/headerimage/generatedHeaderImage-1776736992912.jpg"/&gt;&lt;p&gt;Confluent introduces a new approach in Apache Kafka that moves schema IDs from message payloads to record headers, aiming to simplify schema governance and evolution. The update integrates with Schema Registry, improves compatibility across serialization formats, and reduces coupling between data and metadata in event-driven architectures.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Protocol Buffers</category>
      <category>Event Stream Processing</category>
      <category>Schema</category>
      <category>Avro</category>
      <category>Data Analytics</category>
      <category>Apache Flink</category>
      <category>Data Pipelines</category>
      <category>Streaming</category>
      <category>JSON</category>
      <category>Apache Kafka</category>
      <category>Machine Learning</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Fri, 01 May 2026 14:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/confluent-kafka-header-schema-id/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Machine+Learning</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-05-01T14:06:00Z</dc:date>
      <dc:identifier>/news/2026/05/confluent-kafka-header-schema-id/en</dc:identifier>
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