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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
TFX is an end-to-end platform for deploying production ML pipelines
| Date | Stars |
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| 2026-07-24 | 2189 |
| 2026-07-25 | 2189 |
| 2026-07-28 | 2189 |
| 2026-07-30 | 2189 |
| 2026-08-06 | 2189 |
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<!-- See: www.tensorflow.org/tfx/ --> # TFX [](https://github.com/tensorflow/tfx) [](https://badge.fury.io/py/tfx) [](https://www.tensorflow.org/tfx) [TensorFlow Extended (TFX)](https://tensorflow.org/tfx) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using [Apache Airflow](https://airflow.apache.org/) and [Kubeflow Pipelines](https://www.kubeflow.org/). Both the components themselves as well as the integrations with orchestration systems can be extended. TFX components interact with a [ML Metadata](https://github.com/google/ml-metadata) backend that keeps a record of component runs, input and output artifacts, and runtime configuration. This metadata backend enables advanced functionality like experiment tracking or warmstarting/resuming ML models from previous runs.  ## Documentation ### User Documentation Please see the [TFX User Guide](https://github.com/tensorflow/tfx/blob/master/docs/guide/index.md). ### Development References #### Roadmap The TFX [Roadmap](https://github.com/tensorflow/tfx/blob/master/ROADMAP.md), which is updated quarterly. #### Release Details For detailed previous and upcoming changes, please [check here](https://github.com/tensorflow/tfx/blob/master/RELEASE.md) #### Requests For Comment TFX is an open-source project and we strongly encourage active participation by the ML community in helping to shape TFX to meet or exceed their needs. An important component of that effort is the RFC process. Please see the listing of [current and past TFX RFCs](RFCs.md). Please see the [TensorFlow Request for Comments (TF-RFC)](https://github.com/tensorflow/community/blob/master/governance/TF-RFCs.md) process page for information on how community members can contribute. ## Examples * [Chicago Taxi Example](https://github.com/tensorflow/tfx/tree/master/tfx/examples/chicago_taxi_pipeline) ## Compatible versions The following table describes how the `tfx` package versions are compatible with its major dependency PyPI packages. This is determined by our testing framework, but other *untested* combinations may also work. tfx | Python | apache-beam[gcp] | ml-metadata | pyarrow | tensorflow | tensorflow-data-validation | tensorflow-metadata | tensorflow-model-analysis | tensorflow-serving-api | tensorflow-transform | tfx-bsl ------------------------------------------------------------------------- | -------------------- | ---------------- | ----------- | ------- | ----------------- | -------------------------- | ------------------- | ------------------------- | ---------------------- | -------------------- | ------- [GitHub master](https://github.com/tensorflow/tfx/blob/master/RELEASE.md) | >=3.10,<3.13 | 2.73.0 | 1.21.0 | 18.1.0 | nightly (2.x) | 1.21.0 | 1.21.0 | 0.52.0 | 2.19.1 | 1.21.0 | 1.21.0 [1.21.0](https://github.com/tensorflow/tfx/blob/v1.21.0/RELEASE.md) | >=3.10,<3.13 | 2.73.0 | 1.21.0 | 18.1.0 | 2.21 | 1.21.0 | 1.21.0 | 0.52.0 | 2.19.1 | 1.21.0 | 1.21.0 [1.17.2](https://github.com/tensorflow/tfx/blob/v1.17.2/RELEASE.md) | >=3.9,<3.11 | 2.59.0 | 1.17.1 | 10.0.1 | 2.17 | 1.17.0 | 1.17.1 | 0.48.0 | 2.17.1 | 1.17.0
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Peyton Murray · OpenTeams (formerly QuanSight) · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:30ac12bc309f43f0, topic:tensorflow