TFX 4 | Ingesting Parquet files in TFX Pipeline

In this article, we will discuss how to ingest Parquet files in a TFX pipeline using Python, TensorFlow, and Machine Learning Operations (MLOps) practices.

Introduction to TFX

TFX, or TensorFlow Extended, is an end-to-end platform for deploying production-ready machine learning pipelines powered by TensorFlow. It provides a set of tools for building, training, and deploying ML models in a scalable and reliable manner.

Ingesting Parquet files in TFX Pipeline

Parquet is a columnar storage format that is commonly used in data processing pipelines due to its efficient storage and fast query performance. Ingesting Parquet files in a TFX pipeline allows you to work with large datasets efficiently.

Here is an example code snippet showing how to ingest Parquet files in a TFX pipeline:

import tensorflow as tf
import tensorflow_data_validation as tfdv
from tfx.components import FileBasedExampleGen

example_gen = FileBasedExampleGen(input_base="path/to/parquet_files")

This code snippet creates a FileBasedExampleGen component in the TFX pipeline, which reads data from Parquet files located in the specified input base directory.

Conclusion

Ingesting Parquet files in a TFX pipeline is essential for working with large datasets efficiently. By following the steps outlined in this article, you can leverage the power of Parquet files in your machine learning pipelines.