InfoQ Homepage Machine Learning Content on InfoQ
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InfoQ AI, ML and Data Engineering Trends Report - 2025
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.
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How Causal Reasoning Addresses the Limitations of LLMs in Observability
Large language models excel at converting observability telemetry into clear summaries but struggle with accurate root cause analysis in distributed systems. LLMs often hallucinate explanations and confuse symptoms with causes. This article suggests how causal reasoning models with Bayesian inference offer more reliable incident diagnosis.
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/articles/mcp-connector-for-building-smarter-modular-ai-agents/en/smallimage/mcp-connector-for-building-smarter-modular-ai-agents-thumbnail-1756277862571.jpg)
MCP: the Universal Connector for Building Smarter, Modular AI Agents
In this article, the authors discuss Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data they need. They also talk about how MCP empowers agent development, and its adoption in leading open-source frameworks.
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/articles/ai-infrastructure-aggregating-agentic-traffic/en/smallimage/ai-infrastructure-aggregating-agentic-traffic-thumbnail-1755682463373.jpg)
The Missing Layer in AI Infrastructure: Aggregating Agentic Traffic
In this article, author Eyal Solomon discusses AI Gateways, the outbound proxy servers that intercept and manage AI-agent-initiated traffic in real time to enforce policies and provide central management.
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Faster, Smoother, More Engaging: Personalized Content Pagination
Dynamic content loading powered by AI transforms user experiences by personalizing delivery based on user's behavior and network conditions. By analyzing scroll depth, speed, and dwell time, we optimize loading times, enhance engagement, and reduce infrastructure costs, especially on devices with poor internet connectivity.
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/articles/practical-design-patterns-modern-ai-systems/en/smallimage/practical-design-patterns-modern-ai-systems-thumbnail-1747122253617.jpg)
Beyond the Gang of Four: Practical Design Patterns for Modern AI Systems
In this article, author Rahul Suresh discusses emerging AI patterns in the areas of prompting, responsible AI, user experience, AI-Ops, and optimization, with code examples for each design pattern.
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/articles/best-practices-energy-efficient-ai-ml-systems/en/smallimage/best-practices-energy-efficient-ai-ml-systems-thumbnail-1746711047911.jpg)
Best Practices to Build Energy-Efficient AI/ML Systems
In this article, author Lakshmithejaswi Narasannagari discusses the sustainable innovations in AI/ML technologies, how to track carbon footprint in all stages of ML systems lifecycle and best practices for model development and deployment.
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/articles/building-observable-machine-learning-systems/en/smallimage/beyond-notebook-thumbnail-1741852201404.jpg)
Beyond Notebook: Building Observable Machine Learning Systems
In this article, the author discusses a machine learning pipeline with observability built-in for credit card fraud detection use case, with tools like MLflow, FastAPI, Streamlit, Apache Kafka, Prometheus, Grafana, and Evidently AI.
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/articles/secure-ai-powered-early-detection-system/en/smallimage/designing-architecture-ai-models-thumbnail-1740475990469.jpg)
Secure AI-Powered Early Detection System for Medical Data Analysis & Diagnosis
In this article, the author discusses the techniques for securing AI applications in healthcare with an use case of early detection system for medical data analysis & diagnosis. The proposed layered architecture includes application components to support secure computation, ai modeling, governance and compliance, and monitoring and auditing.
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Building Trust in AI: Security and Risks in Highly Regulated Industries
Explore the transformative power of responsible AI across industries, emphasizing security, MLOps, and compliance. As AI drives innovation—from predicting hurricanes to enhancing legal workflows—organizations must prioritize ethical practices, transparency, and robust governance to safeguard sensitive data while navigating an evolving regulatory landscape.
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/articles/micro-metrics-llm-evaluation/en/smallimage/framework-building-micro-metrics-logo-small-1737371317541.jpg)
A Framework for Building Micro Metrics for LLM System Evaluation
LLM accuracy is a challenging topic to address and is much more multi-dimensional than a simple accuracy score. Denys Linkov introduces a framework for creating micro metrics to evaluate LLM systems, focusing on goal-aligned metrics that improve performance and reliability. By adopting an iterative "crawl, walk, run" methodology, teams can incrementally develop observability.
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Reaching Your Automatic Testing Goals by Enhancing Your Test Architecture
If you have automatic end-to-end tests, you have test architecture, even if you’ve never given it a thought. Test architecture encompasses everything from code to more theoretical concerns like enterprise architecture, but with concrete, immediate consequences. Let's explore how you can achieve the goals you have for your automatic testing effort.