Efficient data handling is fundamental to training machine learning models, particularly when dealing with large datasets where I/O can become a significant bottleneck. TensorFlow's tf.data API offers a powerful and flexible way to build performant input pipelines that decouple data extraction and transformation from model training.

In this chapter, you will learn to use the tf.data API effectively. We will cover:

• Creating tf.data.Dataset objects from various sources, including in-memory arrays (NumPy, Tensors), Python generators, and optimized file formats like TFRecord.
• Applying common data transformations using methods like map() for element-wise preprocessing, batch() for grouping data, shuffle() for randomization, and prefetch() for performance optimization.
• Integrating your custom tf.data pipelines seamlessly with the Keras model.fit() API for training and evaluation.
• Implementing techniques like image data augmentation directly within the pipeline.

Upon completing this chapter, you will be able to construct scalable and efficient data loading mechanisms for your TensorFlow models.