InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Slack's AI-Powered, Hybrid Approach for Large-Scale Migration from Enzyme to React Testing Library
Sergii Gorbachov discusses how Slack saved thousands of hours by using a hybrid AST/LLM approach to automate complex code migration, sharing a transferable model for other companies.
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AI for Food Image Generation in Production: How & Why
Iaroslav Amerkhanov discusses how his team at Delivery Hero leveraged GenAI to generate food images, detailing the architecture, optimization, and business impact.
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Orchestrating AI Services with the Spring AI Framework
Loiane Groner discusses Spring AI and how developers can build powerful, production-ready AI solutions in Java.
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Making AI Agents Work for You (and Your Team)
Hannah Foxwell explains how to design agent teams for high-quality output and shares a vision where AI agents handle toil, freeing humans to focus on creativity and customer relationships.
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10 Reasons Your Multi-Agent Workflows Fail and What You Can Do about It
Victor Dibia discusses multi-agent systems, detailing how to build them with AutoGen, common failure points, and strategic approaches for senior software developers and engineering leaders.
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From Autocomplete to Agents: AI Coding State of Play
Birgitta Böckeler explains how to use AI coding assistants effectively and responsibly, from fighting complacency to fostering a healthy team culture.
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Key Lessons from Shipping AI Products beyond the Hype
Phil Calçado shares key learnings from building and scaling an AI startup, offering a product-centric approach for engineering leaders and architects navigating generative AI.
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The Form of AI
Savannah Kunovsky, who leads IDEO's Emerging Tech Lab, explains how combining engineering rigor with design thinking creates impactful, user-centered AI products.
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Maximizing Deep Learning Performance on CPUs using Modern Architectures
Bibek Bhattarai demystifies Intel AMX, explaining how this CPU architecture accelerates deep learning workloads via low-precision matrix multiplication and efficient data handling.
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Stream and Batch Processing Convergence in Apache Flink
Jiangjie Qin discusses stream and batch processing convergence in Apache Flink, explaining how Flink unifies computing and execution models for enhanced efficiency & reduced data infrastructure costs.
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Enhance LLMs’ Explainability and Trustworthiness with Knowledge Graphs
Leann Chen discusses how knowledge graphs provide structured data to enhance LLM accuracy, tackling common challenges like hallucinations and the "lost-in-the-middle" phenomenon in RAG systems.
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AI Agents & LLMs: Scaling the Next Wave of Automation
The panelists discuss AI agents and LLMs, exploring their definitions, architectures, use cases, reliability, and impact on the SDLC and future of automation.
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