TensorFlow Extended for production

TFX is an end-to-end platform for deploying machine learning pipelines. A part of the TensorFlow ecosystem, it provides a configuration framework and shared libraries so as to integrate the common components needed to define, launch, and monitor software based on ML models. TFX includes many of the requirements for production software deployments and best practices, viz: scalability, consistency, testability, safety and security, and so on.

It starts with ingesting your data, followed by data validation, feature engineering, training, and serving. Google has created libraries for each major phase of the pipeline, and there are frameworks for a wide range of deployment targets. TFX implements a series of ML pipeline components. All of this is made possible by creating horizontal layers for things like pipeline storage, configuration, and orchestration. These layers are very important for managing and optimizing the pipelines and the applications...