Guide to TensorFlow Extended: End-to-End Platform for Deploying Production ML Pipelines

Guide to TensorFlow Extended: End-to-End Platform for Deploying Production ML Pipelines

Tensorflow Extended(TFX) is a production-scaled machine learning platform, taking advantage of the best qualities from Tensorflow and Sibyl. TFX contains a sequence of components to implement ML pipelines that are scalable and give high-performance machine learning tasks.

Ever since Google has publicised Tensorflow, its application in Deep Learning has been increasing tremendously. It is used even more in research and production for authoring ML algorithms. Though it is flexible, it does not provide an end-to-end production system. On the other hand, Sibyl has end-to-end facilities but lacks flexibility. Google then came up with Tensorflow Extended(TFX) idea as a production-scaled machine learning platform on Tensorflow, taking advantage of both Tensorflow and Sibyl frameworks.

TFX contains a sequence of components to implement ML pipelines that are scalable and give high-performance machine learning tasks. These components can also be used independently. Apache Airflow and Kubeflow Pipelines support TFX. TFX components interact with ML Metadata as a 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 warm starting/resuming ML models from previous runs. Compatible versions of TFX can be found here.

TFX’s standard component can be used in the pipeline or individually and provides functionalities to get started with Machine Learning. The diagram below indicates the data flow between different parts. You can learn about various standard features here, in great detail.

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