newsAWS Machine LearningTrust 88 · LabPublished 4d agoLive · 3d ago
Automate replenishment with MMF, Databricks Genie, and Amazon Quick
Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge.
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- PossiblePossibly related (embedding) · 54%CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition →
- PossiblePossibly related (embedding) · 51%amazon-science/chronos-forecasting →
- PossiblePossibly related (embedding) · 47%winedarksea/AutoTS →
- PossiblePossibly related (embedding) · 47%Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters →
- PossiblePossibly related (embedding) · 46%A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting →
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paperCEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decompositionrepoamazon-science/chronos-forecastingrepowinedarksea/AutoTSpaperBet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile ForecasterspaperA Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
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repoamazon-science/chronos-forecastingpaperBet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile ForecasterspaperCEDAR: Controlled and Event-Driven Demand Forecasting via Residual DecompositionpaperA Human-in-the-Loop Autonomous Agent for Industry Time Series Forecastingrepowinedarksea/AutoTS