Netflix builds a Media Understanding Platform for ML-powered dialogue, visual, and shot search across the content catalog
Netflix artists and video editors spent excessive hours on manual pre-work — watching titles start-to-finish to transcribe dialogue with timecodes (watchdowns) and scrubbing footage to find visual elements. Early ML integration systems were bespoke, tightly coupled, and could not scale across algorithms or teams.
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 · Editor submits search query
Studio users submit multimodal queries — text, image, or short video — via gRPC or GraphQL interfaces.
Tools used
MarkenCassandraElasticsearchgRPCGraphQLDomain Graph Service Framework
Outcome
The Media Search Platform (MSP) enables studio creators to find dialogue, visual elements, and similar shots across the Netflix catalog in seconds, while engineers can onboard new ML algorithms independently through a modular, pluggable abstraction layer.
What failed first
Two prior approaches — on-demand batch processing and an online pre-computation system — exposed fundamental scaling problems: disparate systems built by separate teams on different stacks, expensive maintenance, and a tightly coupled architecture that mixed ML algorithms with backend and UI code.
Results
Time savedwhat normally would have taken 1–2 people hours/a full day to do, done in seconds