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    <title>InfoQ - AI, ML &amp; Data Engineering</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ AI, ML &amp; Data Engineering feed</description>
    <item>
      <title>Presentation: Platform Teams Enabling AI - MCP/Multi-Agentic Tools Across Linkedin</title>
      <link>https://www.infoq.com/presentations/ai-multi-agentic-tools/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-multi-agentic-tools/en/mediumimage/medium-1779867927919.jpg"/&gt;&lt;p&gt;LinkedIn’s Karthik Ramgopal and Prince Valluri discuss leveraging AI as a new execution model for large-scale engineering. They explain how to move beyond fragmented implementations by building platform abstractions for orchestration, structured context, and safe tooling like MCP. They share architectural insights from real-world coding, observation, and UI testing agents built at LinkedIn.&lt;/p&gt; &lt;i&gt;By Karthik Ramgopal, Prince Valluri&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>Case Study</category>
      <category>Agents</category>
      <category>Artifacts &amp; Tools</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 05 Jun 2026 12:23:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-multi-agentic-tools/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Karthik Ramgopal, Prince Valluri</dc:creator>
      <dc:date>2026-06-05T12:23:00Z</dc:date>
      <dc:identifier>/presentations/ai-multi-agentic-tools/en</dc:identifier>
    </item>
    <item>
      <title>Dropbox Introduces Nova, an Internal Platform for Running AI Coding Agents at Scale</title>
      <link>https://www.infoq.com/news/2026/06/dropbox-nova-ai-coding-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/dropbox-nova-ai-coding-agents/en/headerimage/generatedHeaderImage-1779952906697.jpg"/&gt;&lt;p&gt;Dropbox has unveiled Nova, an internal platform designed to orchestrate and operationalize AI coding agents across the company's engineering workflows.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>Artificial Intelligence</category>
      <category>AI Assisted Coding</category>
      <category>AI Coding</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 05 Jun 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/dropbox-nova-ai-coding-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-06-05T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/dropbox-nova-ai-coding-agents/en</dc:identifier>
    </item>
    <item>
      <title>Article Series: Securing the AI Stack: From Model to Production</title>
      <link>https://www.infoq.com/articles/secure-ai-stack-model-production-series/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/articles/secure-ai-stack-model-production-series/en/headerimage/Article-Series-Securing-the-AI-Stack-From-Model-to-Production-header-image-1780040531515.jpg"/&gt;&lt;p&gt;This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance.&lt;/p&gt; &lt;i&gt;By Claudio Masolo&lt;/i&gt;</description>
      <category>Article Series</category>
      <category>Artificial Intelligence</category>
      <category>AI Security</category>
      <category>Security</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Fri, 05 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/secure-ai-stack-model-production-series/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Claudio Masolo</dc:creator>
      <dc:date>2026-06-05T09:00:00Z</dc:date>
      <dc:identifier>/articles/secure-ai-stack-model-production-series/en</dc:identifier>
    </item>
    <item>
      <title>Google LiteRT-LM Speeds Up Local Inference Up to 2.2x With Gemma 4 Multi-Token Prediction</title>
      <link>https://www.infoq.com/news/2026/06/google-litertlm-gemma4/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/google-litertlm-gemma4/en/headerimage/google-litert-ml-gemma4-1780649451174.jpeg"/&gt;&lt;p&gt;LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>TensorFlow</category>
      <category>Large language models</category>
      <category>Google</category>
      <category>Gemma</category>
      <category>Edge Computing</category>
      <category>Mobile</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 05 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/google-litertlm-gemma4/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-06-05T09:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/google-litertlm-gemma4/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Choosing Your AI Copilot: Maximizing Developer Productivity</title>
      <link>https://www.infoq.com/presentations/choosing-ai-copilot/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/choosing-ai-copilot/en/mediumimage/medium-1779867439150.jpg"/&gt;&lt;p&gt;Sepehr Khosravi discusses the evolution of developer productivity tools. Evaluating the strengths of tools like Cursor and Claude Code, he explains actionable techniques for senior engineers - including context engineering, custom rules, and Model Context Protocol (MCP) integrations. He shares real-world benchmarks and strategic frameworks for balancing AI adoption with clean code quality.&lt;/p&gt; &lt;i&gt;By Sepehr Khosravi&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 03 Jun 2026 11:05:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/choosing-ai-copilot/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Sepehr Khosravi</dc:creator>
      <dc:date>2026-06-03T11:05:00Z</dc:date>
      <dc:identifier>/presentations/choosing-ai-copilot/en</dc:identifier>
    </item>
    <item>
      <title>Article: Two Misconfigurations That Caused Spark OOM Failures on Kubernetes</title>
      <link>https://www.infoq.com/articles/spark-oom-kubernetes-misconfigurations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/articles/spark-oom-kubernetes-misconfigurations/en/headerimage/spark-oom-kubernetes-misconfigurations-header-1780044756757.jpg"/&gt;&lt;p&gt;After migrating Spark pipelines to Azure Kubernetes Service, two infrastructure settings interacted destructively: spark.kubernetes.local.dirs.tmpfs=true backed shuffle spill with RAM instead of disk, and a hard podAffinity rule forced all executors onto one node. Together, they caused repeated OOM kills invisible to standard diagnostics.&lt;/p&gt; &lt;i&gt;By Pranav Bhasker&lt;/i&gt;</description>
      <category>Cloud</category>
      <category>Apache Spark</category>
      <category>Kubernetes</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Wed, 03 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/spark-oom-kubernetes-misconfigurations/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Pranav Bhasker</dc:creator>
      <dc:date>2026-06-03T09:00:00Z</dc:date>
      <dc:identifier>/articles/spark-oom-kubernetes-misconfigurations/en</dc:identifier>
    </item>
    <item>
      <title>Article: Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG</title>
      <link>https://www.infoq.com/articles/vector-search-hybrid-retrieval-rag/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/articles/vector-search-hybrid-retrieval-rag/en/headerimage/vector-search-hybrid-retrieval-rag-header-1779972811121.jpg"/&gt;&lt;p&gt;In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution.&lt;/p&gt; &lt;i&gt;By Aaditya Chauhan&lt;/i&gt;</description>
      <category>vector databases</category>
      <category>ElasticSearch</category>
      <category>Generative AI</category>
      <category>Retrieval-Augmented Generation</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Tue, 02 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/vector-search-hybrid-retrieval-rag/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Aaditya Chauhan</dc:creator>
      <dc:date>2026-06-02T09:00:00Z</dc:date>
      <dc:identifier>/articles/vector-search-hybrid-retrieval-rag/en</dc:identifier>
    </item>
    <item>
      <title>Claude Code Adds Dynamic Workflows for Parallel Agent Coordination</title>
      <link>https://www.infoq.com/news/2026/06/dynamic-workflows-claude-code/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/dynamic-workflows-claude-code/en/headerimage/generatedHeaderImage-1780332135620.jpg"/&gt;&lt;p&gt;Anthropic introduced Dynamic Workflows, a new capability for Claude Code designed to handle complex software engineering tasks by coordinating large numbers of AI agents within a single workflow.  The feature allows Claude to dynamically create orchestration scripts, break work into subtasks, run them in parallel, and validate results before presenting a final answer.&lt;/p&gt; &lt;i&gt;By Robert Krzaczyński&lt;/i&gt;</description>
      <category>Claude</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Mon, 01 Jun 2026 16:55:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/dynamic-workflows-claude-code/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Robert Krzaczyński</dc:creator>
      <dc:date>2026-06-01T16:55:00Z</dc:date>
      <dc:identifier>/news/2026/06/dynamic-workflows-claude-code/en</dc:identifier>
    </item>
    <item>
      <title>BadHost Vulnerability Exposes AI Agents, Evaluators, and LLM Gateways</title>
      <link>https://www.infoq.com/news/2026/06/badhost-ai-systems-vulnerability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/06/badhost-ai-systems-vulnerability/en/headerimage/badhost-ai-vulnerability-1780322270507.jpeg"/&gt;&lt;p&gt;BadHost is a high-severity authentication bypass vulnerability in the widely used Python web framework Starlette, with 325 million weekly downloads. The flaw allows attackers to use malformed HTTP Host headers to bypass path-based access controls and access sensitive AI agent infrastructure, among other systems.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Python</category>
      <category>Security Vulnerabilities</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Mon, 01 Jun 2026 14:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/06/badhost-ai-systems-vulnerability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-06-01T14:00:00Z</dc:date>
      <dc:identifier>/news/2026/06/badhost-ai-systems-vulnerability/en</dc:identifier>
    </item>
    <item>
      <title>Article: The AI Productivity Paradox in Test Automation: Moving Beyond Structural Validation to Perception and Intent</title>
      <link>https://www.infoq.com/articles/solving-ai-productivity-paradox-test-automation/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/articles/solving-ai-productivity-paradox-test-automation/en/headerimage/solving-ai-productivity-paradox-test-automation-header-1779953915743.jpg"/&gt;&lt;p&gt;The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows how, to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent.&lt;/p&gt; &lt;i&gt;By Amanul Chowdhury, Vinay Gummadavelli&lt;/i&gt;</description>
      <category>Web Development</category>
      <category>Large language models</category>
      <category>JavaScript</category>
      <category>Test Automation</category>
      <category>HTML</category>
      <category>UI Testing</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Mon, 01 Jun 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/solving-ai-productivity-paradox-test-automation/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Amanul Chowdhury, Vinay Gummadavelli</dc:creator>
      <dc:date>2026-06-01T11:00:00Z</dc:date>
      <dc:identifier>/articles/solving-ai-productivity-paradox-test-automation/en</dc:identifier>
    </item>
    <item>
      <title>DuckDB Quack: Client/Server Protocol over HTTP for Multi-User Analytics</title>
      <link>https://www.infoq.com/news/2026/05/duckdb-quack-protocol/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/duckdb-quack-protocol/en/headerimage/generatedHeaderImage-1779460941997.jpg"/&gt;&lt;p&gt;DuckDB has recently announced Quack, a new remote protocol over HTTP that lets multiple DuckDB instances connect to and work with the same database over a network. The protocol introduces client-server capabilities to a database that was previously mostly local and embedded.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>SQL</category>
      <category>Data Analytics</category>
      <category>Distributed Data</category>
      <category>Apache Arrow</category>
      <category>duckdb</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sun, 31 May 2026 11:17:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/duckdb-quack-protocol/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-05-31T11:17:00Z</dc:date>
      <dc:identifier>/news/2026/05/duckdb-quack-protocol/en</dc:identifier>
    </item>
    <item>
      <title>Arm Open-Sources Metis, an AI Security Framework Outperforming Traditional SAST Tools</title>
      <link>https://www.infoq.com/news/2026/05/arm-metis-agentic-security/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/arm-metis-agentic-security/en/headerimage/arm-metis-1780165811953.jpeg"/&gt;&lt;p&gt;Arm has open-sourced Metis, an agentic AI security framework designed to autonomously uncover complex software vulnerabilities. Unlike traditional pattern-based tools, Metis applies semantic reasoning to analyze cross-component dependencies and provides clear, natural language explanations for its findings.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Static Analysis</category>
      <category>Large language models</category>
      <category>ARM</category>
      <category>Security</category>
      <category>Security Vulnerabilities</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Sat, 30 May 2026 19:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/arm-metis-agentic-security/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-05-30T19:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/arm-metis-agentic-security/en</dc:identifier>
    </item>
    <item>
      <title>How Meta Rebuilt Data Ingestion for Petabyte-Scale Reliability</title>
      <link>https://www.infoq.com/news/2026/05/meta-cdc-migration/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/meta-cdc-migration/en/headerimage/generatedHeaderImage-1779134681732.jpg"/&gt;&lt;p&gt;The engineering team at Meta recently outlined how the company migrated a data ingestion platform that transfers several petabytes of MySQL social graph data daily to improve reliability and operational efficiency. The team used techniques like reverse shadowing and continuous checksum monitoring to ensure zero downtime during the transition.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Big Data Infrastructure</category>
      <category>migration</category>
      <category>Facebook</category>
      <category>MySQL</category>
      <category>Scalability</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Sat, 30 May 2026 06:01:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/meta-cdc-migration/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-05-30T06:01:00Z</dc:date>
      <dc:identifier>/news/2026/05/meta-cdc-migration/en</dc:identifier>
    </item>
    <item>
      <title>AI-Assisted Migration Tool Helps Teams Move from ingress-nginx to Higress in Minutes</title>
      <link>https://www.infoq.com/news/2026/05/ai-nginx-higress/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/ai-nginx-higress/en/headerimage/generatedHeaderImage-1779528783880.jpg"/&gt;&lt;p&gt;The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>migration</category>
      <category>NGINX</category>
      <category>Artificial Intelligence</category>
      <category>Infrastructure as Code</category>
      <category>Infrastructure</category>
      <category>AI Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 29 May 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/ai-nginx-higress/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-05-29T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/ai-nginx-higress/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Building Evals for AI Adoption: from Principles to Practice</title>
      <link>https://www.infoq.com/presentations/eval-ai-adoption/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</link>
      <description>&lt;img src="https://res.infoq.com/presentations/eval-ai-adoption/en/mediumimage/medium-1779185675202.jpeg"/&gt;&lt;p&gt;Mallika Rao discusses the hidden risk of evaluation debt in production AI systems, drawing on her experience at Twitter, Walmart, and Netflix. She explains why traditional metrics fail modern architectures, breaks down a five-layer evaluation stack spanning infrastructure and UX, and shares a diagnostic maturity model to help engineering leaders eliminate silent semantic failures.&lt;/p&gt; &lt;i&gt;By Mallika Rao&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Large language models</category>
      <category>Artificial Intelligence</category>
      <category>Adoption</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 29 May 2026 12:00:00 GMT</pubDate>
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      <dc:creator>Mallika Rao</dc:creator>
      <dc:date>2026-05-29T12:00:00Z</dc:date>
      <dc:identifier>/presentations/eval-ai-adoption/en</dc:identifier>
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