Analytics & ML Systems
From raw data to production ML
The problem
You want to move from dashboards to decisions, or from a notebook model to something that runs in production without breaking.
A model that works in a notebook is the easy 20%. The hard part is the production system around it: feature pipelines, versioning, evaluation, monitoring and the analytics layer that makes the output usable. I build that end-to-end.
Examples include market-regime detection (Hidden Markov Models through Wasserstein clustering), central-bank credibility scoring with transformer models, and the ML prediction systems inside Ansaar. I focus on systems that stay correct as data and requirements change.