I work on TFX. TFX started as an internal Google project [1] but over the years we've been steadily open sourcing more and more of it. Portability and interoperability are key focus areas for our project, more information in [2]. TFX OSS currently consists of libraries, components, and some binaries. It is up to the developer to pick the right level of abstraction for the task at hand. More information about this in [3]. Many of the ideas in TFX were inspired by an earlier production ML system at Google called Sibyl, more information about Sibyl in [4]. The two systems combined represent over a decade of production ML experience of many engineers and we hope other engineers will find it useful, too.

Also of interest may be this overview blog post [5] by yours truly :)

[1] ACM KDD '17 "TFX: A TensorFlow-Based Production-Scale Machine Learning Platform" https://dl.acm.org/doi/10.1145/3097983.3098021 [2] https://www.tensorflow.org/tfx/guide#portability_and_interop... [3] https://www.youtube.com/watch?v=zxd3Q2gdArY [4] https://www.youtube.com/watch?v=3SaZ5UAQrQM [5] https://blog.tensorflow.org/2019/05/research-to-production-w...