InfoQ Homepage Artificial Intelligence Content on InfoQ
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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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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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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.
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Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how CPUs and GPUs can be utilized.
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Building Guardrails for Enterprise AI Applications W/ LLMs
Shreya Rajpal introduces Guardrails AI, an open-source platform designed to mitigate risks and enhance the safety and efficiency of LLMs.
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Combating AI-Generated Fake Images with JavaScript Libraries
Kate Sills discusses JavaScript libraries to use for cryptographic hashes, digital signatures and timestamping, the traditional archival process, and how cryptographic hashes can prevent tampering.
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Generative AI: Shaping a New Future for Fraud Prevention
Neha Narkhede discusses a vision for fraud and risk management that leverages the advancements in generative AI.
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Responsible AI: from Principle to Practice!
Mehrnoosh Sameki discusses Responsible AI best practices to apply in a machine learning lifecycle and shares open source tools to incorporate to implement Responsible AI in practice.
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Needle in a 930M Member Haystack: People Search AI @LinkedIn
Mathew Teoh explores how LinkedIn's People Search system uses ML to surface the right person that you're looking for.
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