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TFX OSS roadmap

This highlights the main OSS efforts for the TFX team in 2019. If you're interested in contributing in one of these areas, contributions are always welcome, especially in areas that extend TFX into infrastructure currently not widely in use at Google.

Vision

  • Democratize access to machine learning (ML) best practices, tools, and code.
  • Enable users to easily run production ML pipelines on public clouds, on premises, and in heterogeneous computing environments.

Goals

  • Help enterprises realize large-scale production ML capabilities similar to what we have available at Google. We recognize that every enterprise has unique infrastructure challenges, and we want TFX to be open and adaptable to those challenges.
  • Stimulate innovation: Machine learning is a rapid, innovative field and we want TFX to help researchers and engineers both realize and contribute to that innovation. Likewise, we want TFX to be interoperable with other ML efforts in the open source community.
  • Usability: We want the journey to deploy a model in production to be as frictionless as possible throughout the entire journey -- from the initial efforts building a model to the final touches of deploying in production.

Specific efforts underway

Extensibility
  • Enable additional modularity and extensibility across TFX, including the ability for users to inject callbacks for TFX executors, create custom executors, components and pipelines. Encourage the discovery and reuse of these new contributions.
  • Participate in and extend support for other OSS efforts, initially: Apache Beam, ML Metadata, Kubeflow, Tensorboard, and TensorFlow 2.0.
  • Extend portability across additional cluster computing frameworks, orchestrators, and data representations.
Performance
Usability
  • Support of TensorFlow 2.0, starting with Keras.
  • Integration with TensorBoard and Jupyter notebooks.
  • A unified command line interface (CLI) for users to perform critical user journeys in different environments.
  • Improving the testing capabilities for OSS developers.
  • Lightweight local orchestrator.
  • Increased interoperability with Kubeflow Pipelines.
Education
  • More pipeline code examples, including DIY orchestrators and custom components.
  • Incorporate community feedback (RFCs) to the TFX design review process.
Innovation and collaboration
  • Formalize Special Interest Groups (SIGs) for specific aspects of TFX to accelerate community innovation and collaboration.
  • Early access to new features.

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