Metaflow is a high level scientific workflow system that allows users to create complex data processing pipelines that integrate multiple technologies into a single unified workflow. It is designed to make it easier for data scientists to specify complex scientific experiments, and automate the workflow of data analysis and machine learning.
Metaflow began as an internal project at Netflix in 2017, which has since been made open source in 2019 and is now used by many organizations including Google, IBM, Microsoft, and Uber. The core goal of Metaflow is to provide a platform for developers to easily create and manage complex data processing workflows. It simplifies the process of creating and managing data pipelines by allowing users to specify their workflow in a simple and flexible Python-like language.
The key features of Metaflow include data versioning, adaptive resource utilization, multiple storage support, parallelized execution, and visual data flow diagrams. Data versioning allows users to keep track of the versions of their data sets, and perform repro-runs without requiring manual work. Adaptive resource utilization enables the system to optimize its resources usage, and support for several storage types such as S3, GCP, and local storage makes it easier for users to manage their data. Moreover, the visual data flow diagrams make it simpler to understand the complex workflows, and share them with others.
Metaflow offers integrations for a wide variety of services including databases, machine learning frameworks, and cloud services. This allows users to build complex pipelines that integrate components from different services, without having to manually coordinate between them. In addition, it also comes with a suite of built-in components such as TensorFlow, PyTorch, and Scikit-Learn that can be easily plugged into a Metaflow pipeline.
Metaflow is an open source system that is both easy to use and highly extensible. It helps data scientists save time and effort when building complex workflows, and is gradually becoming the go-to tool for creating and managing data pipelines.
