InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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From ms to µs: OSS Valkey Architecture Patterns for Modern AI
Dumanshu Goyal explains how moving from proxied architectures to direct access in Redis/Valkey slashes tail latency to microseconds, lowers costs, and eliminates single points of failure.
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The Five Stages of AI Maturity in Engineering Organizations - Where and Why Teams Get Stuck
Quotient CEO Lizzie Matusov explores why surging enterprise AI spend isn't driving faster software delivery, introducing a 5-stage AI maturity framework to unblock bottlenecks across the SDLC.
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Architecting AI Systems for the Messy Reality of Enterprises: Why Agentic Compute is the Missing Layer
Arun Joseph explains how to successfully build and deploy enterprise-grade agentic platforms by leveraging existing teams, JVM stacks, and a "compute as the agent" architectural paradigm.
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Getting Rid of LeetCode Interviews in the World of AI
Daniel Doubrovkine shares why LeetCode interviews are obsolete in the AI era and discusses how engineering leaders must adapt hiring loops to evaluate real-world system design and AI collaboration.
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The Future of Engineering: Mindsets That Matter When Code Isn’t Enough
Ben Greene shares lessons from startups for software engineers navigating AI coding agents. He explains why starting simple, keeping comprehension, and prioritizing customer impact matter.
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Autonomous Data Products for the Autonomous Era: Rethinking Data Architecture for GenAI
Jörg Schad discusses how to bridge the gap between prototypes and production by standardizing data access for GenAI, using autonomous data products to prevent context rot and ensure safety.
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From Copy-Paste to Composition: Building Agents Like Real Software
Jake Mannix explains how to mature AI agents using "virtual tools" for encapsulation, interface abstraction, and deterministic taint tracking to prevent lethal data exfiltration risks.
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Engineering AI for Creativity and Curiosity on Mobile
Bhavuk Jain discusses scaling raw AI models into production-grade mobile features. He explains the architecture, runtime guardrails, and fine-tuning behind AI Wallpapers and Circle to Search.
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From OTEL to SLMs: Distilling Frontier Model Behaviour from Production Telemetry
Ben O'Mahony shares how to build a data flywheel for AI agents using OpenTelemetry, explaining how behavioral user signals can be mined to distill and fine-tune smaller, localized language models.
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Postgres for Production Agents: Your Relational Foundation for Enterprise AI
Gwen Shapira discusses how to power enterprise AI features using Postgres, explaining advanced relational SQL context retrieval, pgvector semantic tuning, and transitioning to agentic workflows.
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Chaos Engineering GPU Clusters
Bryan Oliver explains how to apply chaos engineering to massive GPU clusters. Learn how to handle hardware variability, NUMA nodes, and network faults to secure your AI infrastructure.
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Accelerating Netflix Data: a Cross-Team Journey from Offline to Online
Raj Ummadisetty and Ken Kurzweil explain how Netflix transitioned its key-value abstraction from stateless to stateful, reducing data deployment time by 90% and infrastructure costs by 70%.
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