InfoQ Homepage Performance Content on InfoQ
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The Infrastructure Challenge behind Production AI
The panelists share how core data systems and cloud platforms handle machine-driven workloads, exploring infrastructure design patterns and failure points as AI becomes a permanent fixture.
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Realtime and Batch Processing of GPU Workloads
Joseph Stein explains how to build a highly available private AI cloud. He shares blueprints on scaling vLLM on enterprise GPUs, implementing gateway guardrails, and optimizing batch workloads.
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The AI Gateway: Scaling Centralized Inference across Decentralized Teams
Meryem Arik explains how AI model gateways resolve the chaos of decentralized engineering teams by centralizing inference. Learn to optimize costs, enforce governance, and maximize GPU utilization.
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Using AI as a Thinking Partner for Large-Scale Engineering Systems
Google senior staff engineer Julie Qiu shares how she uses AI as a thinking partner to navigate large-scale systems, moving beyond code generation to architecting complex, multi-language ecosystems.
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The Human Scalability Problem: Why Your Teams Don’t Scale Like Your Code
Charlotte de Jong Schouwenburg explains why scaling engineering teams often slows down delivery. She shares how to solve human latency by building trust and psychological safety across silos.
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Speed at Scale: Optimizing the Largest CX Platform out There
Matheus Albuquerque explains how to modernize legacy React codebases using jscodeshift, code splitting, and Preact to achieve a 37% bundle reduction while maintaining support for older browsers.
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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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Scaling to 100+ as a Director: Lessons from Growing Engineering Organizations
Thiago Ghisi discusses his journey scaling engineering orgs to 100+ people. He shares a 3-level impact framework and explains how to evolve from a "solver" to a "driver" of organizational growth.
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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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Theme Systems at Scale: How to Build Highly Customizable Software
Guilherme Carreiro explains how Shopify scales a customizable theme system to 60M requests per minute. Learn about Liquid DSL constraints, native extensions, and bridging technical/non-technical gaps.
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From Confusion to Clarity: Advanced Observability Strategies for Media Workflows at Netflix
Naveen Mareddy and Sujana Sooreddy explain how Netflix monitors massive media encoding workflows. They discuss scaling to 1M+ trace spans and using Flink to unlock real-time business insights.
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Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges
Matthias Niehoff discusses bridging the gap between application and data engineering. Learn to apply software engineering best practices, embrace boring technologies, and simplify architecture.
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