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Published : Mar 29, 2022
NOT ON THE CURRENT EDITION
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Mar 2022
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Metaflow is a user-friendly Python library and back-end service that helps data scientists and engineers build and manage production-ready data processing, ML training and inference workflows. Metaflow provides Python APIs that structure the code as a directed graph of steps. Each step can be decorated with flexible configurations such as the required compute and storage resources. Code and data artifacts for each step's run (aka task) are stored and can be retrieved either for future runs or the next steps in the flow, enabling you to recover from errors, repeat runs and track versions of models and their dependencies across multiple runs.

The value proposition of Metaflow is the simplicity of its idiomatic Python library: it fully integrates with the build and run-time infrastructure to enable running data engineering and science tasks in local and scaled production environments. At the time of writing, Metaflow is heavily integrated with AWS services such as S3 for its data store service and step functions for orchestration. Metaflow supports R in addition to Python. Its core features are open sourced.

If you're building and deploying your production ML and data-processing pipelines on AWS, Metaflow is a lightweight full-stack alternative framework to more complex platforms such as MLflow.

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