Developing and optimizing a TensorFlow model is a significant step, but getting that model into a reliable production environment presents a distinct set of challenges. Consistency across data preprocessing, training, evaluation, and serving is necessary for dependable results. This requires managing the entire machine learning workflow, not just the model code itself.

This chapter introduces TensorFlow Extended (TFX), an end-to-end platform for deploying production ML pipelines. We will examine how TFX provides a structured framework to manage the lifecycle of machine learning models, ensuring reproducibility and scalability.

You will learn to:

• Understand the architecture and purpose of TFX for production systems.
• Utilize standard TFX components for tasks like data ingestion (ExampleGen), data validation (StatisticsGen, SchemaGen), feature engineering (Transform), model training (Trainer), evaluation (Evaluator), and deployment validation (Pusher).
• Assemble these components into a cohesive pipeline.
• Grasp the concepts behind orchestrating TFX pipelines using common workflow managers.

By working through the TFX components and their interactions, you will gain the skills to build more manageable and automated machine learning systems suitable for production environments.