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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>Mini book: Next-Gen Architecture Playbook: Insights and Patterns for the AI Era</title>
      <link>https://www.infoq.com/minibooks/next-gen-architecture-ai-era/?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/minibooks/next-gen-architecture-ai-era/en/smallimage/thumb-image-emag-next-gen-architecture-playbook-1787922032442.jpg"/&gt;&lt;p&gt;This eMag examines how architects can lead with clarity in a rapidly evolving engineering world, distilling industry insights into field-tested practices for teams. Together, these stories reveal a core theme: the technology leader’s role is expanding from building systems to guiding how tech behaves and learns, while enabling engineers and organizations to bring out their best.&lt;/p&gt; &lt;i&gt;By InfoQ&lt;/i&gt;</description>
      <category>Platform Engineering</category>
      <category>AI Architecture</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>minibook</category>
      <pubDate>Fri, 04 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/minibooks/next-gen-architecture-ai-era/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>InfoQ</dc:creator>
      <dc:date>2026-09-04T11:00:00Z</dc:date>
      <dc:identifier>/minibooks/next-gen-architecture-ai-era/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From S3 to GPU in One Copy: Rethinking Data Loading for ML Training</title>
      <link>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?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/vortex-columnar-file-format-gpu-streaming/en/mediumimage/onur-satici-medium-1787813397611.jpeg"/&gt;&lt;p&gt;Onur Satici explains how Vortex, an open-source columnar file format under the Linux Foundation, revolutionizes high-throughput data loading. He details how cascading lightweight encodings, layout-based segment pruning, and zero-copy memory pipelines eliminate CPU/NVMe bottlenecks to stream S3 data straight to GPUs at speeds up to 60 Gbps without requiring upfront data reprocessing.&lt;/p&gt; &lt;i&gt;By Onur Satici&lt;/i&gt;</description>
      <category>Architecture</category>
      <category>Columnar Databases</category>
      <category>Data Lake</category>
      <category>GPU</category>
      <category>Machine Learning</category>
      <category>S3</category>
      <category>Streaming</category>
      <category>Transcripts</category>
      <category>QCon London 2026</category>
      <category>Data Pipelines</category>
      <category>CUDA</category>
      <category>Rust</category>
      <category>Performance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 04 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Onur Satici</dc:creator>
      <dc:date>2026-09-04T11:00:00Z</dc:date>
      <dc:identifier>/presentations/vortex-columnar-file-format-gpu-streaming/en</dc:identifier>
    </item>
    <item>
      <title>Copilot Code Review Reaches Azure Repos, Billed Per Review with Reporting Two Days Behind</title>
      <link>https://www.infoq.com/news/2026/09/copilot-code-review-azure-repos/?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/2026/09/copilot-code-review-azure-repos/en/headerimage/generatedHeaderImage-1788266643329.jpg"/&gt;&lt;p&gt;Microsoft opened GitHub Copilot code review for Azure Repos to all Azure DevOps customers, after acknowledging that many are not ready to migrate to GitHub. Reviews bill per use through the linked Azure subscription and appear in Cost Management 48 hours later. Budgets notify but do not stop reviews, and concurrency caps at five per organization.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Azure</category>
      <category>Continuous Delivery</category>
      <category>Generative AI</category>
      <category>Developer Experience</category>
      <category>Cloud</category>
      <category>Cost Optimization</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Fri, 04 Sep 2026 10:01:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/copilot-code-review-azure-repos/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-09-04T10:01:00Z</dc:date>
      <dc:identifier>/news/2026/09/copilot-code-review-azure-repos/en</dc:identifier>
    </item>
    <item>
      <title>Shopify Introduces Gisting: Compressing LLM System Prompts into Learned Tokens</title>
      <link>https://www.infoq.com/news/2026/09/spotify-gisting-llm-performance/?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/2026/09/spotify-gisting-llm-performance/en/headerimage/spotify-app-size-growth-process-1788462838475.jpeg"/&gt;&lt;p&gt;Shopify's engineering introduced gisting, a novel technique for compressing long LLM prompts into a smaller set of learned "gist" tokens, improving throughput and reducing inference cost.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Model Inference</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Performance</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 03 Sep 2026 20:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/spotify-gisting-llm-performance/?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>2026-09-03T20:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/spotify-gisting-llm-performance/en</dc:identifier>
    </item>
    <item>
      <title>Cohere’s Parse 5 Promises Efficient Multi-Modal Information Extraction from Complex Documents</title>
      <link>https://www.infoq.com/news/2026/09/cohere-multimodal-parse/?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://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Cohere has launched Parse 5, a multimodal foundation model designed to extract structured data from complex enterprise documents. The 2.3-billion-parameter system converts visually rich PDFs into Markdown while providing bounding box coordinates for visual grounding. It has been evaluated against over 2,000 enterprise pages, achieving an average score of 79.2 in key performance areas.&lt;/p&gt; &lt;i&gt;By Olimpiu Pop&lt;/i&gt;</description>
      <category>OCR</category>
      <category>Machine Learning</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Thu, 03 Sep 2026 06:06:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/cohere-multimodal-parse/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Olimpiu Pop</dc:creator>
      <dc:date>2026-09-03T06:06:00Z</dc:date>
      <dc:identifier>/news/2026/09/cohere-multimodal-parse/en</dc:identifier>
    </item>
    <item>
      <title>Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value</title>
      <link>https://www.infoq.com/news/2026/09/swiggy-pltv-multitask-mlp/?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/2026/09/swiggy-pltv-multitask-mlp/en/headerimage/generatedHeaderImage-1787507980584.jpg"/&gt;&lt;p&gt;Swiggy developed an in-house predicted lifetime value model using more than 350 pre order features and a multi task MLP for Food and Instamart. Adding order count as an auxiliary task reduced model parameters by 63% while improving predictive performance. The pLTV signal is used with Google Target ROAS bidding to optimize customer acquisition.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Google</category>
      <category>Customers &amp; Requirements</category>
      <category>Continuous Delivery</category>
      <category>Acquisition</category>
      <category>Neural Networks</category>
      <category>Machine Learning</category>
      <category>Online Learning</category>
      <category>Software Engineering</category>
      <category>Advertising</category>
      <category>Modeling</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Wed, 02 Sep 2026 13:55:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/swiggy-pltv-multitask-mlp/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-02T13:55:00Z</dc:date>
      <dc:identifier>/news/2026/09/swiggy-pltv-multitask-mlp/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Beyond Prompting: Context Engineering for Production-Grade AI</title>
      <link>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?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/context-engineering-redis-llm-architecture/en/mediumimage/ricardo-ferreira-medium-1787820318843.jpg"/&gt;&lt;p&gt;Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints.&lt;/p&gt; &lt;i&gt;By Ricardo Ferreira&lt;/i&gt;</description>
      <category>Retrieval-Augmented Generation</category>
      <category>Transcripts</category>
      <category>Architecture</category>
      <category>Redis</category>
      <category>Caching</category>
      <category>Generative AI</category>
      <category>QCon AI Boston 2026</category>
      <category>LangChain4j</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Wed, 02 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Ricardo Ferreira</dc:creator>
      <dc:date>2026-09-02T11:00:00Z</dc:date>
      <dc:identifier>/presentations/context-engineering-redis-llm-architecture/en</dc:identifier>
    </item>
    <item>
      <title>Cloudflare Adds Optional OAuth Scopes, Letting Developers Mark What Users May Decline</title>
      <link>https://www.infoq.com/news/2026/09/cloudflare-optional-oauth-scopes/?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/2026/09/cloudflare-optional-oauth-scopes/en/headerimage/generatedHeaderImage-1787915759251.jpg"/&gt;&lt;p&gt;Cloudflare has added optional OAuth scopes, letting client owners mark which permissions users may deselect at consent. The company names MCP servers as the motivating case, since agents request the union of everything they might do. Partial consent exists elsewhere, but developer control over which scopes are droppable does not.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Security</category>
      <category>Architecture</category>
      <category>Access Control</category>
      <category>Cloud</category>
      <category>Cloudflare</category>
      <category>Agents</category>
      <category>Standardization</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Wed, 02 Sep 2026 09:07:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/cloudflare-optional-oauth-scopes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-09-02T09:07:00Z</dc:date>
      <dc:identifier>/news/2026/09/cloudflare-optional-oauth-scopes/en</dc:identifier>
    </item>
    <item>
      <title>OpenClaw 2.0 Releases with Simplified Setup and Collaborative Agents</title>
      <link>https://www.infoq.com/news/2026/09/openclaw-2-release/?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/2026/09/openclaw-2-release/en/headerimage/generatedHeaderImage-1788278004063.jpg"/&gt;&lt;p&gt;OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features.&lt;/p&gt; &lt;i&gt;By Daniel Dominguez&lt;/i&gt;</description>
      <category>OpenAI</category>
      <category>Anthropic</category>
      <category>Artificial Intelligence</category>
      <category>ChatGPT</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Claude</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 18:47:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/openclaw-2-release/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Daniel Dominguez</dc:creator>
      <dc:date>2026-09-01T18:47:00Z</dc:date>
      <dc:identifier>/news/2026/09/openclaw-2-release/en</dc:identifier>
    </item>
    <item>
      <title>InfoQ previews the September Cohorts of its Online Certification Programs</title>
      <link>https://www.infoq.com/news/2026/09/infoq-online-cohorts-sept-2026/?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/2026/09/infoq-online-cohorts-sept-2026/en/headerimage/InfoQ-news-cohorts-1788263191137.jpg"/&gt;&lt;p&gt;A preview of the September cohorts of the InfoQ Online Certification Programs, and the facilitators leading them: Luca Mezzalira, Michelle Brush, Zichuan Xiong, and Premanand Chandrasekaran.&lt;/p&gt; &lt;i&gt;By Artenisa Chatziou&lt;/i&gt;</description>
      <category>InfoQ Certification Program</category>
      <category>Architecture ICSAET</category>
      <category>Engineering Leadership Certification</category>
      <category>AI Security &amp; Privacy Engineering Certification</category>
      <category>AI Engineering Certification</category>
      <category>AI-Assisted Engineering Certification</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Culture &amp; Methods</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 13:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/infoq-online-cohorts-sept-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Artenisa Chatziou</dc:creator>
      <dc:date>2026-09-01T13:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/infoq-online-cohorts-sept-2026/en</dc:identifier>
    </item>
    <item>
      <title>HCP Terraform Positions Itself as the Control Plane for AI-Driven Infrastructure</title>
      <link>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?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/2026/09/hcp-terraform-ai-driven-control/en/headerimage/generatedHeaderImage-1787561761498.jpg"/&gt;&lt;p&gt;HashiCorp is positioning HCP Terraform as the governance and control plane for a new generation of AI-driven infrastructure, arguing that the rapid adoption of coding agents is shifting the biggest infrastructure challenge from writing configuration to verifying and safely executing it.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>AI Architecture</category>
      <category>Artificial Intelligence</category>
      <category>AI Development</category>
      <category>Terraform</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?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>2026-09-01T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/hcp-terraform-ai-driven-control/en</dc:identifier>
    </item>
    <item>
      <title>DoorDash’s Flux Runs 130,000 Engineering Tasks through Cloud-Based Agents</title>
      <link>https://www.infoq.com/news/2026/08/doordash-flux-cloud-agent/?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://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;DoorDash has moved engineering agent workloads from developer laptops to its Flux cloud platform. The platform automated 130,000 engineering tasks in one month and supports more than 25,000 automated code reviews weekly. Flux uses isolated Firecracker microVMs, an MCP gateway, reusable playbooks, and multiple invocation surfaces to run agent workflows with scoped access and centralized auditing.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Continuous Improvement</category>
      <category>Infrastructure</category>
      <category>AI Security</category>
      <category>AI Development</category>
      <category>AI Harness</category>
      <category>Cloud</category>
      <category>Code Reviews</category>
      <category>Agents</category>
      <category>AI Coding</category>
      <category>VM</category>
      <category>Software Engineering</category>
      <category>AI Architecture</category>
      <category>Developer Experience</category>
      <category>AIOps</category>
      <category>Continuous Deployment</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 31 Aug 2026 14:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/doordash-flux-cloud-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-08-31T14:28:00Z</dc:date>
      <dc:identifier>/news/2026/08/doordash-flux-cloud-agent/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: Scott Jenson on Evolving Desktop OS, Local-First, &amp; Agentic UX</title>
      <link>https://www.infoq.com/podcasts/evolving-desktop-agentic-ux/?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/podcasts/evolving-desktop-agentic-ux/en/smallimage/infoq-podcast-500-1787734073812.jpg"/&gt;&lt;p&gt;In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models.&lt;/p&gt; &lt;i&gt;By Scott Jenson&lt;/i&gt;</description>
      <category>UX</category>
      <category>Local First</category>
      <category>Artificial Intelligence</category>
      <category>The InfoQ Podcast</category>
      <category>Design</category>
      <category>Large language models</category>
      <category>Operating Systems</category>
      <category>Desktop</category>
      <category>Privacy</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Culture &amp; Methods</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>podcast</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/evolving-desktop-agentic-ux/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Scott Jenson</dc:creator>
      <dc:date>2026-08-31T11:00:00Z</dc:date>
      <dc:identifier>/podcasts/evolving-desktop-agentic-ux/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Running AI at the Edge: Running Real Workloads Directly in the Browser</title>
      <link>https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?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/local-ai-browser-inference-privacy/en/mediumimage/james-hall-medium-1787813225372.jpeg"/&gt;&lt;p&gt;James Hall discusses the strategic and technical imperative of moving AI workloads from cloud providers to local edge devices. He shares practical approaches using WebGPU, Transformers.js, and DuckDB to achieve near-native performance in JavaScript. Through real-world case studies, he explains how to minimize data privacy risks, optimize browser inference, and build rigorous evaluation suites.&lt;/p&gt; &lt;i&gt;By James Hall&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Edge Computing</category>
      <category>QCon London 2026</category>
      <category>AI Security</category>
      <category>Cloud Computing</category>
      <category>GPU</category>
      <category>Web Browser</category>
      <category>Machine Learning</category>
      <category>Local Inference</category>
      <category>Web Development</category>
      <category>Privacy</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Mon, 31 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/local-ai-browser-inference-privacy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>James Hall</dc:creator>
      <dc:date>2026-08-31T11:00:00Z</dc:date>
      <dc:identifier>/presentations/local-ai-browser-inference-privacy/en</dc:identifier>
    </item>
    <item>
      <title>Foundry Model Router Expands from Two Regions to 28, Refreshing Its Model Pool</title>
      <link>https://www.infoq.com/news/2026/08/foundry-model-router-regions/?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/2026/08/foundry-model-router-regions/en/headerimage/generatedHeaderImage-1787836928169.jpg"/&gt;&lt;p&gt;Microsoft expanded Foundry's model router from two regions to 28 for global standard and 21 for data zone deployments, while adding Claude Opus 4.8 and GPT-5.6 and removing four deprecated models. Default deployments receive pool changes automatically; configured subsets exclude new models until added. The effective context window equals the smallest model in the pool.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Microsoft Azure</category>
      <category>Azure</category>
      <category>AI Architecture</category>
      <category>Generative AI</category>
      <category>Cloud</category>
      <category>Cost Optimization</category>
      <category>Governance</category>
      <category>Microsoft</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 31 Aug 2026 10:18:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/foundry-model-router-regions/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI%2C+ML+%26+Data+Engineering</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-08-31T10:18:00Z</dc:date>
      <dc:identifier>/news/2026/08/foundry-model-router-regions/en</dc:identifier>
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