TensorFlow Extended (TFX) is an end-to-end platform for building and deploying production machine learning (ML) pipelines. It provides a set of components and libraries that enable teams to create scalable and maintainable ML pipelines that include data validation, preprocessing, model training, evaluation, and serving.
The key components of TFX are:
Data validation: TFX provides the TensorFlow Data Validation (TFDV) library, which allows you to define a schema for your data and validate it before training your models. TFDV can help you detect anomalies and errors in your data, such as missing values, incorrect data types, or unexpected distributions.
Preprocessing: TFX includes the TensorFlow Transform (TFT) library, which allows you to perform data preprocessing at scale. TFT provides a set of reusable functions for common preprocessing tasks, such as scaling, normalization, and feature engineering. It also allows you to apply the same preprocessing steps to both training and serving data.
Training: TFX supports both TensorFlow Estimators and Keras models for training your ML models. It provides a set of standard hyperparameters and training configurations that can be easily customized for your specific use case. TFX also includes the TensorFlow Model Analysis (TFMA) library, which provides tools for evaluating and visualizing the performance of your models.
Evaluation: TFX provides several ways to evaluate your models, including TFMA, which allows you to compare the performance of multiple models using metrics such as accuracy, precision, and recall. TFX also includes the TensorFlow Serving component, which allows you to serve your models for online inference.
Serving: TFX supports TensorFlow Serving, which is a high-performance serving system for machine learning models. TensorFlow Serving allows you to deploy your trained models to production and serve them at scale with low latency and high throughput. TFX also supports other serving options, such as TensorFlow Lite for mobile devices and TensorFlow.js for web applications.
TFX provides a standardized approach to building ML pipelines that can be easily scaled and maintained. It allows teams to collaborate on building and deploying ML models, while also ensuring that best practices are followed for data preprocessing, model training, evaluation, and serving.