InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Open Source, Community, and Consequence: the Story of MongoDB
Andrew Davidson and Akshat Vig share the "MongoDB Way", discussing how to disrupt a 50-year database legacy by leveraging the document model, community trust, and rapid, public iteration.
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Data Mesh in Action: a Journey from Ideation to Implementation
Anurag Kale explains how to solve brittle ETL pipelines using Data Mesh. He shares an approach to decentralizing data ownership, treating data as a product, and building self-serve platforms.
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From Symptom Checkers to Smart Chatbots: the Role of AI in Virtual Care
Andre Ribeiro discusses the architecture of Healthily’s AI symptom checker. He explains how Bayesian inference and RAG models bridge the gap between medical insights and confident patient action.
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Four Patterns of AI Native Development
Patrick Debois explains the shift to AI-native development, focusing on how engineers are moving from producers to managers of intent while navigating the "chaos period" of 600+ emerging AI tools.
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Busting AI Myths and Embracing Realities in Privacy & Security
Katharine Jarmul keynotes on common myths around privacy and security in AI and explores what the realities are, covering design patterns that help build more secure, more private AI systems.
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AI Innovation in 2025 and beyond
Tejas Kumar discusses the evolution of AI from 1906 to 2026, explaining how agentic RAG and the Model Context Protocol (MCP) are shifting the industry from complex UIs to a prompt-driven future.
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DevOps Modernization: AI Agents, Intelligent Observability and Automation
The panelists explain how AI is redefining DevOps and SRE practices by moving teams beyond reactive monitoring toward predictive, automated delivery and operations.
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Panel: Modern Data Architectures
The panelists emphasize that data engineering is no longer just about "click-and-drag" UI tools; it is software engineering applied to data.
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Building Embedding Models for Large-Scale Real-World Applications
Sahil Dua explains the architecture and training of embedding models. He shares practical tips for distilling large models and scaling RAG applications for real-time production environments.
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Beyond the Warehouse: Why BigQuery Alone Won’t Solve Your Data Problems
Sarah Usher explains why relying solely on a data warehouse fails at scale. She shares a 3-layer data lifecycle (Raw, Curated, Use Case) to help engineering leaders build flexible, decoupled systems.
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Foundation Models for Ranking: Challenges, Successes, and Lessons Learned
Moumita Bhattacharya explains how Netflix unifies search and recommendations using the "UniCoRn" model and leverages Transformer-based foundation models to personalize the experience for 300M+ users.
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How to Unlock Insights and Enable Discovery within Petabytes of Autonomous Driving Data
Kyra Mozley explains Perception 2.0, shifting from rigid CV pipelines to semantic embeddings. She shares how Wayve uses foundation models & vector search to solve the edge case "needle in a haystack."
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