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      <title>Lyft Scales Global Localization Using AI and Human-in-the-Loop Review</title>
      <link>https://www.infoq.com/news/2026/04/lyft-ai-localization-pipeline/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Batch+Processing-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/04/lyft-ai-localization-pipeline/en/headerimage/generatedHeaderImage-1775411926263.jpg"/&gt;&lt;p&gt;Lyft has implemented an AI-driven localization system to accelerate translations of its app and web content. Using a dual-path pipeline with large language models and human review, the system processes most content in minutes, improves international release speed, ensures brand consistency, and handles complex cases like regional idioms and legal messaging efficiently.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>i18n</category>
      <category>localization</category>
      <category>Data Pipelines</category>
      <category>Web</category>
      <category>Batch Processing</category>
      <category>Internationalization</category>
      <category>Automation</category>
      <category>Translation</category>
      <category>Large language models</category>
      <category>Real Time</category>
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      <pubDate>Mon, 13 Apr 2026 13:45:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/04/lyft-ai-localization-pipeline/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Batch+Processing-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-04-13T13:45:00Z</dc:date>
      <dc:identifier>/news/2026/04/lyft-ai-localization-pipeline/en</dc:identifier>
    </item>
    <item>
      <title>Pinterest Reduces Spark OOM Failures by 96% through Auto Memory Retries</title>
      <link>https://www.infoq.com/news/2026/04/pinterest-spark-oom-reduction/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Batch+Processing-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/04/pinterest-spark-oom-reduction/en/headerimage/workflow-1775338668860.jpeg"/&gt;&lt;p&gt;Pinterest Engineering cut Apache Spark out-of-memory failures by 96% using improved observability, configuration tuning, and automatic memory retries. Staged rollout, dashboards, and proactive memory adjustments stabilized data pipelines, reduced manual intervention, and lowered operational overhead across tens of thousands of daily jobs.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Big Data</category>
      <category>Optimization</category>
      <category>Cost Optimization</category>
      <category>Memory</category>
      <category>Architecture Analysis</category>
      <category>Batch Processing</category>
      <category>Observability</category>
      <category>Architecture</category>
      <category>Distributed Systems</category>
      <category>Apache Spark</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Mon, 06 Apr 2026 14:32:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/04/pinterest-spark-oom-reduction/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Batch+Processing-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-04-06T14:32:00Z</dc:date>
      <dc:identifier>/news/2026/04/pinterest-spark-oom-reduction/en</dc:identifier>
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