2026-06-07 –, Hardwick Hub
Forecasts live in a high-dimensional space. They vary by origin date, prediction horizon, scenario assumptions, uncertainty, granularity and decision context. Treating them as a single artefact creates ambiguity, semantic drift and misaligned expectations.
In this talk, I’ll show how we reframed forecasting at Spotify as a structured prediction problem not just a modelling task. I’ll cover practical design patterns for representing forecast objects across origins and scenarios, handling probabilistic outputs, implementing hierarchical reconciliation and tracking lineage and versioning in Python-based systems.
Aimed at data scientists and ML engineers working with production systems, this talk offers a framework for thinking about forecast dimensionality and concrete implementation patterns you can apply in your own forecasting platforms.
Thomas Ogden is a Senior Data Scientist in Platform at Spotify. He builds tools, mostly with probabilistic machine learning on sequences and graphs.