InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Powering Enterprise AI Applications with Data and Open Source Software
Francisco Javier Arceo explored Feast, the open-source feature store designed to address common data challenges in the AI/ML lifecycle, such as feature redundancy, and low-latency serving at scale.
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Securing AI Assistants: Strategies and Practices for Protecting Data
Andra Lezza reviews the OWASP Top 10 for LLMs and contrasts security controls for independent vs. integrated copilot architectures.
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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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Humans in the Loop: Engineering Leadership in a Chaotic Industry
Michelle Brush discusses engineering leadership in the age of AI/ML and automation.
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AI-Driven Software Delivery: Leveraging Lean, ChOP & LLMs to Create More Effective Learning Experiences at QCon
Wes Reisz details building a RAG-powered QCon certification in 4 weeks. He dives into the serverless pipeline, RAG architecture, lessons on using supervised coding agents, and Lean thinking.
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Growing and Cultivating Strong Machine Learning Engineers
Vivek Gupta explains how to nourish and cultivate Machine Learning engineers, detailing the unique production-ML skills required for scaling, governance, and LLMOps.
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Achieving Precision in AI: Retrieving the Right Data Using AI Agents
Adi Polak discusses achieving precision in GenAI by moving beyond RAG to Agentic RAG. She details agent patterns, feedback loops, and using data streaming architectures to scale real-time AI.
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Vector Sync Patterns: Keeping AI Features Fresh When Your Data Changes
Ricardo Ferreira discusses five advanced Vector Sync Patterns to tackle multi-dimensional vector staleness & integration challenges in modern AI/microservices architectures.
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Why Observability Matters (More!) with AI Applications
Sally O'Malley shares how to build an AI observability stack with open-source tools (Prometheus, Grafana, OpenTelemetry, Tempo, vLLM/Llama Stack). Learn to track performance, quality and cost signals.
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Deploy MultiModal RAG Systems with vLLM
Stephen Batifol explains the core concepts of multimodal RAG systems, vector search indexes (HNSW, IVF), and embedding model selection. He details vLLM and Pixtral for optimized inference..
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Beyond the Hype: Architecting Systems with Agentic AI
The panelists discuss architecting with Agentic AI: the hype, the reality, and how to build production-ready, trustworthy systems with guardrails, observability, and continual relevance.
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Scaling an Embedded Database for the Cloud – Challenges and Trade-Offs
Stephanie Wang discusses the tradeoffs and lessons learned from building a cloud-native data warehouse by scaling an embedded database, going from an in-process system to one with cloud capabilities.
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