InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Sam Partee discusses Vector embeddings in LLMs, a tool capable of capturing the essence of unstructured data used by LLMs to gain access to a wealth of contextually relevant knowledge.
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Being a Responsible Developer in the Age of AI Hype
Justin Sheehy discusses the dramatic developments in some areas of artificial intelligence and the need for the responsible use of AI systems.
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Defensible Moats: Unlocking Enterprise Value with Large Language Models
Nischal HP discusses risk mitigation, environmental, social, and governance (ESG) framework implementation to achieve sustainability goals, strategic procurement, spend analytics, data compliance.
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When AIOps Meets MLOps: What it Takes to Deploy ML Models at Scale
Ghida Ibrahim introduces the concept of AIOps referring to using AI and data-driven tooling to provision, manage and scale distributed IT infra.
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Reach Next-Level Autonomy with LLM-Based AI Agents
Tingyi Li discusses the AI Agent, exploring how it extends the frontiers of Generative AI applications and leads to next-level autonomy in combination with enterprise data.
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Lessons Learned from Building LinkedIn’s AI Data Platform
Felix GV provides an overview of LinkedIn’s AI ecosystem, then discusses the data platform underneath it: an open source database called Venice.
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Unpacking How Ads Ranking Works @Pinterest
Aayush Mudgal discusses social media advertising, unpacking how Pinterest harnesses the power of Deep Learning Models and big data to tailor relevant advertisements to the pinners.
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LIquid: a Large-Scale Relational Graph Database
Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.
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The AI Revolution Will Not Be Monopolized: How Open-Source Beats Economies of Scale, Even for LLMs
Ines Montani discusses why the AI space won’t be monopolized, covering the open-source model, common misconceptions about use cases for LLMs in industry, and principles of software development.
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Retrieval-Augmented Generation (RAG) Patterns and Best Practices
Jay Alammar discusses the common schematics of RAG systems and tips on how to improve them.
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Understanding Architectures for Multi-Region Data Residency
Alex Strachan discusses challenges to build multi-region data storages, understanding why and when a business needs to do this, who are the real stakeholders, and who owns what.
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Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
Loubna Ben Allal discusses Large Language Models (LLMs), exploring the current developments of these models, how they are trained, and how they can be leveraged with custom codebases.
