Netflix scales Match Cutting ML pipeline across its entire catalog using Amber media ML infrastructure
Netflix's media ML practitioners struggled with inconsistent media access, expensive repeated computations across independent pipelines, and bespoke triggering components that were hard to maintain — preventing the Match Cutting pipeline from scaling beyond single titles.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · New video file trigger
Amber automatically initiates scoring for new videos as soon as standardized video encodes are ready.
The Amber infrastructure standardized media access, eliminated redundant computation through feature memoization, and enabled Match Cutting to scale across the entire Netflix catalog with automatic triggering for new videos; the GPU training cluster throughput increased 3–5 times.
What failed first
The original Match Cutting pipeline lacked input file standardization causing quality issues for cross-title matching, bespoke triggering components caused unnecessary re-computation and inconsistencies, and the quadratic pair computation made scaling to cross-catalog matching computationally intractable.