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Meta-ML: Supercharging ML Productivity at Scale with AI-Native Infrastructure#

@Scale: AI & Data 2026 digest Program Adjacent pick 17 June 2026

Abstract

Niv Taiber, Bar Ulman, Ravali Busetty, Aasim Rab, Brant Swidler (Meta)

  • Why: Making legacy infrastructure callable by agents — without rebuilding it — is the operational question every platform team now faces, and this gives the architectural patterns plus adoption numbers from a very large fleet.
  • What: An AI-native layer exposing Meta's legacy ML stack (data discovery through serving and capacity planning) as 200+ agent-callable tools, with 59% power-user adoption, 4M+ tool calls/week, and debugging workflows cut from 1.5 hours to under 10 minutes (from abstract only).
  • How: Three patterns: progressive tool disclosure (agents discover capabilities on demand instead of loading 200+ tools into context), federated domain-team ownership (15+ infra teams expose their systems as tools, mapped to MLE jobs-to-be-done), and a production-grounded eval flywheel instrumenting real MLE sessions for test cases synthetic benchmarks miss.
  • Where applicable: The patterns are explicitly framed as transferring to any organization with legacy ML systems — the progressive-disclosure and federated-ownership ideas are general agent-platform design. Adoption numbers are Meta's and depend on having a large internal MLE population.