Netflix introduces Metaflow spin for notebook-like iterative ML/AI development
ML and AI development involves slow iteration cycles due to long-running data transformations, model training, and stochastic processes, and the existing Metaflow resume command restarted execution from a selected step, introducing latency rather than enabling near-instant single-step feedback.
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 · Create and run flow skeleton
A minimal flow skeleton is created and run once to persist test artifacts.
The new spin command in Metaflow 2.19 executes a single @step with state carried over from the parent step, making development as smooth as a notebook while producing a production-ready, scalable workflow; AI coding agents using spin surface and fix errors more rapidly.
What failed first
The existing resume command restarted execution from the selected step onward, introducing latency between iterations, unlike notebooks that allow near-instant feedback by reusing data held in memory.