Data/ML Workflow Orchestration Platform

Netflix is evolving its data and ML workflow orchestration capabilities by introducing Maestro, a next-generation, horizontally scalable, and user-friendly platform. Maestro replaces the previous Meson system to address scale limitations and improve usability for a diverse range of users, including data scientists, engineers, and content producers. It supports complex DAGs, event-driven triggers, and provides a workflow-as-a-service model. This post details the configurable nature of Metaflow, a popular Python library for building and deploying data science workflows, and its integration with Maestro. It highlights how Metaflow's configuration system, including its support for environment variables, JSON configuration files, and command-line arguments, allows for flexible deployment and execution across different environments. The post also touches upon the benefits of this configurability for managing complex data pipelines and ensuring reproducibility.