Machine learning teams today are drowning in massive volumes of raw, redundant data that inflate training costs, slow down experimentation, and degrade model quality. The core architectural flaw is that we apply control too late—after the data has already been moved into centralized stores or training clusters—creating waste, instability, and long iteration cycles. What if we could fix this problem right at the source?

In this talk, we’ll discuss an open-source playbook for shifting ML data filtering, transformation, and governance upstream, directly where data is generated. We’ll walk through a declarative, policy-as-code framework for building distributed pipelines that intelligently discard noise, balance datasets, and enrich signals before they ever reach your model training infrastructure.

Drawing from real-world ML workflows, we’ll show how this “upstream control” approach can reduce dataset size by 50–70%, cut model onboarding time in half, and embed reproducibility and compliance directly into the ML lifecycle—rather than patching them in afterward.

Attendees will leave with: - A mental model for analyzing and optimizing the ML data supply chain. - An understanding of open-source tools for declarative, source-level ML data controls. - Actionable strategies to accelerate iteration, lower training costs, and improve model outcomes.