InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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/articles/beyond-offset-lag-kafka-apache-hudi/en/smallimage/beyond-offset-lag-kafka-apache-hudi-thumbnail-1787577734265.jpg)
Beyond Offset Lag: Computing Time in Queue for Apache Hudi Data Lake Pipelines at Petabyte Scale
In this article, author Srikanth Mamidala discusses the data lake architecture used for analytics, reporting, and machine learning and shows how to manage the consumer lag metrics when using Kafka and Apache Hudi.
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/articles/agentic-fitness-functions-evolutionary-architecture/en/smallimage/thumbnail-1786428822427.jpg)
Agentic Fitness Functions: Extending Evolutionary Architecture Beyond Deterministic Rules
Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops.
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/articles/ai-workflow-pattern/en/smallimage/thumbnail-1785766770921.jpg)
Runtime-Agnostic AI Workflows: a Pattern for Production Durability and Fast Eval Iteration
AI workflows have two needs that trade off directly. Running reliably in production requires persisting and distributing every step so it survives crashes, deploys, and restarts. But that same machinery is what makes runs too heavy for the fast, throwaway loop you need to check an LLM's output quality. The properties that buy durability are the ones that kill iteration speed.
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/articles/securing-mcp-production-gateway/en/smallimage/securing-mcp-production-gateway-thumbnail-1784884301110.jpg)
Securing MCP in Production: Defense-in-Depth beyond the Gateway
This article presents a defense-in-depth approach for securing Model Context Protocol (MCP) deployments in production. It outlines four architectural control layers: safe execution, management infrastructure, outbound trust, and semantic integrity, arguing that production security requires enforcement beyond the gateway at the earliest trustworthy control points.
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/articles/evolutionary-architecture-pattern/en/smallimage/evolutionary-architecture-pattern-thumbnail-1784798616611.jpg)
An Evolutionary Architecture Pattern for Managing AI’s Pace of Change
Traditional API gateways assume deterministic services and simple schemas - assumptions agentic AI breaks. Discover why enterprise engineering leaders are adopting AI Gateways as an evolutionary architecture seam. Centralize guardrails, model routing, agent identity, action policy, and semantic audit within a single control plane to prevent costly incidents while keeping core platforms stable.
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/articles/multi-agent-security-operations/en/smallimage/Multi-Agent-AI-for-Production-Security-Operations-thumb-1784628007582.jpg)
Multi-Agent AI for Production Security Operations: an A2A and MCP Architecture in a 5G Core
The bottleneck in a mature SOC is rarely analyst triage; rather, it is the detection-engineering team's ability to keep the rule base aligned with a threat landscape that evolves faster than rules can be written. Learn how multi-agent system for production security operations has reduced mean times to detect and to respond by 40% and compressed the human work required by 12x.
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/articles/security-ai-threat-evolution/en/smallimage/security-ai-threat-evolution-thumbnail-1782202845102.jpg)
Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution
This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows.
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/articles/understanding-ml-model-poisoning/en/smallimage/thumbnail-understanding-ml-model-poisoning-1781597719188.jpg)
Understanding ML Model Poisoning: How it Happens and How to Detect it
In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines.
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/articles/governing-ai-cloud-guide/en/smallimage/governing-ai-cloud-guide-thumbnail-1781010249930.jpg)
Governing AI in the Cloud: a Practical Guide for Architects
In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes.
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/articles/artificial-intelligence-driven-phishing/en/smallimage/thumbnail-artificial-intelligence-driven-phishing-1780562847202.jpg)
Artificial Intelligence-Driven Phishing: How Phishing Technique Is Evolving and Implemented
In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness.
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/articles/adoption-curve-twenty/en/smallimage/20-birthday-InfoQ-thumb-image-updated-1780931679034.jpg)
The Technology Adoption Curve, Twenty Years On
Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.
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/articles/secure-ai-stack-model-production-series/en/smallimage/Article-Series-Securing-the-AI-Stack-From-Model-to-Production-thumb-image-1780040531515.jpg)
Article Series: Securing the AI Stack: from Model to Production
This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance.