InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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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.
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Streaming Databases: Embracing the Convergence of Stream Processing and Databases
Yingjun Wu discusses the evolution of streaming databases, and the features and design principles that set streaming databases apart from conventional database systems and stream processing engines.
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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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Platform and Features MLEs, a Scalable and Product-Centric Approach for High Performing Data Products
Massimo Belloni discusses the lessons learnt in the last couple of years around organizing a Data Science Team and the Machine Learning Engineering efforts at Bumble Inc.
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