  As Machine Learning (ML) gains adoption across industries and new use cases,
practitioners increasingly realize the challenges around effectively developing
and iterating on ML systems: reproducibility, debugging, scalability, and
documentation are elusive goals for real-world pipelines outside tech-first
companies. In this paper, we review the nature of ML-oriented workloads and
argue that re-purposing existing tools won't solve the current productivity
issues, as ML peculiarities warrant specialized development tooling. We then
introduce Metaflow, an open-source framework for ML projects explicitly
designed to boost the productivity of data practitioners by abstracting away
the execution of ML code from the definition of the business logic. We show how
our design addresses the main challenges in ML operations (MLOps), and document
through examples, interviews and use cases its practical impact on the field.
