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Artifact-ML

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Overview

Artifact-ML eliminates imperative glue code in ML experiments by providing the tools to build reusable workflows declaratively.

By reusable, we refer to workflows that are defined once with the potential to be reused by any compatible model.

By declarative, we refer to building through expressing high-level intent---rather than catering to implementation details.

Topics

  • Packages — overview of the packages comprising the framework.
  • Getting Started — quick installation instructions.
  • Value Proposition — high-level description of the problem addressed by the project.
  • Motivating Example — a concrete example illustrating the problem (and solution) in action.
  • Design Philosophy — the core ideas and principles that shape the framework.
  • Domain Toolkits — a note on the project's organization by application domain.