『Data Science in the Wild』のカバーアート

Data Science in the Wild

Data Science in the Wild

著者： Peter Liu
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Data Science in the Wild turns each day's featured deep-dive article from this knowledge bank into a conversational audio deep dive. Every episode unpacks one real production system from a top engineering team — Netflix, Uber, Spotify, Airbnb, Meta, Pinterest, and more — covering the business problem, the technical approach, and the impact, so you can absorb the substance of a serious applied ML/AI system on your commute.

It is data science in the wild: not textbook theory, but the models, architectures, and hard trade-offs that real companies actually ship.
エピソード
  • Airbnb — Turning Scientific Method Into AI Infrastructure

    Airbnb — Turning Scientific Method Into AI Infrastructure
    2026/09/17
    Airbnb needed to understand the rare, risky edge cases a new AI customer-service assistant might run into — the kind of thing that used to take a data scientist months of manually reading through support transcripts, one judgment call at a time. So Airbnb built Insight Miner: an agent harness that doesn't just run an LLM over the data, but wraps it in an explicit scientific-methodology layer — extract, embed, cluster, then classify — so the process of investigation, not just the answer, is reproducible and auditable by someone other than the original analyst. We walk through how the harness is built, why "methodology as infrastructure" is the real idea here rather than any single new technique, and how investigations that used to take months now take days — with usage spreading from a handful of data scientists to dozens of non-technical teams across the company. Source article: "Beyond the Model: Engineering AI Infra with Scientific Judgement" — Airbnb Tech Blog, https://airbnb.tech/ai-ml/beyond-the-model-engineering-ai-infra-with-scientific-judgement/ (published 2026-09).
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    21 分
  • Spotify — Why Bayesian A/B Testing Doesn't Pay Off

    Spotify — Why Bayesian A/B Testing Doesn't Pay Off
    2026/09/17
    Bayesian A/B testing gets pitched as more intuitive, safer to peek at early, and automatically smarter about multiple metrics — so why didn't Spotify adopt it? This episode walks through Spotify's own math: under the flat priors most platforms actually ship, Bayesian and frequentist testing produce numerically identical results, and the "advantages" people cite either require infrastructure most teams don't have (calibrated priors, Bayes-factor stopping, hundreds of historical experiments) or quietly reduce to standard frequentist practice anyway. We cover the business context (why experimentation trustworthiness matters more than framework fashion), the statistical reasoning (posterior equivalence, the winner's curse, decision theory), and what it means for teams facing the same "should we go Bayesian?" question. Source article: "Why Spotify Is Not Using Bayesian A/B Testing" — Spotify Engineering, https://engineering.atspotify.com/2026/9/why-spotify-is-not-using-bayesian-a-b-testing (published 2026-09).
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    24 分
  • Instacart — Can an AI Agent Out-Model Your Best Data Scientist?

    Instacart — Can an AI Agent Out-Model Your Best Data Scientist?
    2026/09/15
    Instacart's machine learning engineers ran an experiment most teams are quietly wondering about: what happens when you let a coding agent loose not just on your pipeline code, but on the actual modeling work — picking features, trying architectures, tuning hyperparameters — for models that have already been optimized by humans for months? The results were a genuine surprise in places: on some of Instacart's most mature, hardest-to-improve production models, agent-driven runs delivered 3-5% offline error reductions among their most promising attempts, holding up against a battery of randomization checks before anyone trusted them. But the team is just as candid about the other side — agents with broad permissions in a modeling environment are a real safety surface, not a hypothetical one, and they're recommending sandboxing and compliance awareness even after their own trial runs went mostly smoothly. Source article: "Agentic Machine Learning Modeling at Instacart" — Instacart Tech Blog, https://tech.instacart.com/agentic-machine-learning-modeling-at-instacart-fb3ecd295ee7 (published 2026-09).
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    19 分
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