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Tag: quality

  • Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code

    Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code Understand air quality: access the available data, interpret data types, and execute starter codes The post Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code appeared first on Towards Data Science. Prithviraj…

  • How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k

    How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k The third and final part for evaluating the retrieval quality of your RAG pipeline with graded measures The post How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k appeared first on Towards Data Science. Maria Mouschoutzi Go to…

  • How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)

    How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) Evaluating the retrieval quality of your RAG pipeline with binary, order-aware measures The post How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) appeared first on Towards Data…

  • Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity

    Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity Understand how open source can help you unravel air quality The post Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity appeared first on Towards Data Science. Prithviraj Pramanik Go to original source

  • Measuring Sample Quality with Copula Discrepancies

    Measuring Sample Quality with Copula Discrepancies arXiv:2507.21434v1 Announce Type: new Abstract: The scalable Markov chain Monte Carlo (MCMC) algorithms that underpin modern Bayesian machine learning, such as Stochastic Gradient Langevin Dynamics (SGLD), sacrifice asymptotic exactness for computational speed, creating a critical diagnostic gap: traditional sample quality measures fail catastrophically when applied to biased samplers. While…

  • LLM Evaluations: from Prototype to Production

    LLM Evaluations: from Prototype to Production Evaluation is the cornerstone of any machine learning product. Investing in quality measurement delivers significant returns. Let’s explore the potential business benefits. As management consultant and writer Peter Drucker once said, “If you can’t measure it, you can’t improve it.” Building a robust evaluation system helps you identify areas…

  • Measuring the Cost of Production Issues on Development Teams

    Measuring the Cost of Production Issues on Development Teams Deprioritizing quality sacrifices both software stability and velocity, leading to costly issues. Investing in quality boosts speed and outcomes. Image by the author. (AI generated Midjourney) Investing in software quality is often easier said than done. Although many engineering managers express a commitment to high-quality software,…