InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Infusing AI into Your Java applications
Equip yourself with the basic AI knowledge and skills you need to start building intelligent and responsive Enterprise Java applications. With the help of our simple chatbot application for booking interplanetary space trips, see how Java frameworks like LangChain4j with Quarkus make it easy and efficient to interact with LLMs and create satisfying applications for end-users.
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Building Reproducible ML Systems with Apache Iceberg and SparkSQL: Open Source Foundations
Traditional data lakes are great for storing massive amounts of stuff, but they're terrible at the transactional guarantees and versioning that ML workloads desperately need. Apache Iceberg and SparkSQL bring database-like reliability to your data lake. Time travel, schema evolution, and ACID transactions help support reproducible machine learning experiments.
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A First-Timer’s Guide to Curating a Technical Conference Track
One first-time track host shares the process, constraints, and takeaways from building a track from scratch at QCon London 2025.
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Optimizing Search Systems: Balancing Speed, Relevance, and Scalability
Innovative software engineer focused on optimizing search performance in dynamic environments. This article highlights key strategies from our QCon San Francisco 2024 presentation, addressing challenges faced by platforms like Uber Eats in data indexing and retrieval. Our advancements ensure swift, relevant user experiences amidst ever-growing datasets.
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Agentic AI Architecture Framework for Enterprises
To deploy agentic AI responsibly and effectively in the enterprise, organizations must progress through a three-tier architecture, Foundation tier, Workflow tier, and Autonomous tier where trust, governance, and transparency precede autonomy.
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Effective Practices for Coding with a Chat-Based AI
In this article, we explore how AI agents are reshaping software development and the impact they have on a developer’s workflow. We introduce a practical approach to staying in control while working with these tools by adopting key best practices from the discipline of software architecture, including defining an implementation plan, splitting tasks, and so on.
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Why Is My Docker Image So Big? A Deep Dive with ‘dive’ to Find the Bloat
AI images typically bloat from massive library installations and base OS components, with large Docker images slowing AI development and increasing costs. Chirag Agrawal demonstrates how to diagnose bloat using Docker's history and the interactive 'dive' tool to examine each layer in detail. The article shows how effective diagnosis leads to targeted optimizations.
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The State Space Solution to Hallucinations: How State Space Models are Slicing the Competition
AI-powered search tools often hallucinate and make up facts, misquote sources, and recycle outdated information. The real cause of this is tied to the architecture of most AI models: Transformer. In this article, author Albert Lie explains why transformers struggle with hallucinations, how State Space Models (SSMs) offer a solution, and what this shift could mean for the future of AI search.
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Spotting Image Differences in Visual Software Testing with AI
Current AI, including multimodal models, fails at robust visual regression testing, missing structural changes that pixel-based tools flag as false positives. This article proposes a CNN-based solution to compare image segments, tolerating minor displacements. For larger distortions, a multi-scale algorithm realigns the images before comparison, isolating the true differences.
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AI Interventions to Reduce Cycle Time in Legacy Modernization
In this article, we share our experiences and insights on how large language models (LLMs) helped us uncover and enhance the conceptual constructs behind software. We discuss how these approaches address the inherent complexity of software engineering and improve the likelihood of success in large, complex software modernization projects.
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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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Large Concept Models: a Paradigm Shift in AI Reasoning
Differently from LLMs, Large Concept Models (LCMs) use structured knowledge to grasp relationships between concepts, enhancing the decision-making process and providing a transparent reasoning audit trail. Using LCMs with LLMs can facilitate building AI systems that can analyze complex scenarios and effectively communicate insights, driving towards developing more reliable and explainable AI.