TFX 9 | Splitting Dataset in TFX Pipeline

In this article, we will explore how to split a dataset in a TFX pipeline using TensorFlow Extended (TFX). Splitting a dataset into training and evaluation sets is a common practice in machine learning to assess the performance of a model.

TFX provides tools and components that allow you to preprocess, train, and evaluate machine learning models in a production-ready pipeline. In this tutorial, we will focus on splitting the dataset before training the model in the TFX pipeline.

Splitting Dataset in TFX Pipeline

To split a dataset in a TFX pipeline, you can use the CsvExampleGen component to read the data from CSV files and split it into training and evaluation sets. You can specify the split_config parameter to define the ratio of the training and evaluation sets.


from tfx.components import CsvExampleGen
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
context = InteractiveContext()
example_gen = CsvExampleGen(input_base=data_root, input_config=... , splits_config=...)
context.run(example_gen)


After splitting the dataset, you can use the StatisticsGen component to compute statistics for the training and evaluation sets. These statistics can be used to analyze and understand the data distribution before training the model.


from tfx.components import StatisticsGen
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)


Conclusion

In this article, we learned how to split a dataset in a TFX pipeline using the CsvExampleGen component. By splitting the dataset into training and evaluation sets, we can evaluate the performance of our model and make improvements before deploying it in production.

TFX provides a set of components and tools that streamline the machine learning workflow and make it easier to develop and deploy models. By leveraging the capabilities of TFX, you can build scalable and reliable ML pipelines for your projects.