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As Machine Learning (ML) gains adoption across industries and new use cases,practitioners increasingly realize the challenges around effectively developingand iterating on ML systems: reproducibility, debugging, scalability, anddocumentation are elusive goals for real-world pipelines outside tech-firstcompanies. In this paper, we review the nature of ML-oriented workloads andargue that re-purposing existing tools won't solve the current productivityissues, as ML peculiarities warrant specialized development tooling. We thenintroduce Metaflow, an open-source framework for ML projects explicitlydesigned to boost the productivity of data practitioners by abstracting awaythe execution of ML code from the definition of the business logic. We show howour design addresses the main challenges in ML operations (MLOps), and documentthrough examples, interviews and use cases its practical impact on the field.

Reasonable Scale Machine Learning with Open-Source Metaflow