Feature Engineering with TensorFlow TFX

3 min readAug 8, 2023

Feature engineering plays a pivotal role in building accurate and robust machine learning models. It involves transforming raw data into meaningful features that help algorithms better understand patterns and relationships. In this blog post, we’ll explore how TensorFlow TFX (TensorFlow Extended) can streamline the feature engineering process within a comprehensive end-to-end ML pipeline.

What is TensorFlow TFX?

TensorFlow TFX is a powerful framework that simplifies the process of developing production-ready machine learning pipelines. It provides a set of tools and components designed to manage the entire machine learning workflow, from data ingestion to model deployment. One crucial aspect of this workflow is feature engineering, which TFX handles seamlessly through its dedicated components.

ExampleGen: Data Ingestion and Conversion

The journey begins with ExampleGen, which takes care of ingesting raw data and converting it into a format suitable for TensorFlow operations. It automatically splits the data into training and evaluation sets, making it easy to assess model performance. Additionally, ExampleGen stores the examples in TFRecord format, optimizing data read and write operations.

example_gen = CsvExampleGen(input_base='data_root')

StatisticsGen: Compute Data Statistics

Once the data is ingested, StatisticsGen steps in to compute valuable statistics over the dataset. By leveraging the TensorFlow Data Validation library, it provides insights into the data’s distribution and characteristics. These statistics are essential not only for analysis but also for downstream components that rely on a deeper understanding of the data.

compute_eval_stats = StatisticsGen(
      examples=example_gen.outputs['examples'],
      name='compute-eval-stats'
      )

SchemaGen: Generate Data Schema

StatisticsGen’s output is then used by SchemaGen to generate a schema that defines the expected structure and properties of the features. This schema serves as a contract between the data producer and consumer, ensuring that the data remains consistent and aligned with expectations throughout the pipeline.

schema_gen = tfx.components.SchemaGen(
    statistics=stats_gen.outputs['statistics'])

ExampleValidator: Validate Data against Schema

To maintain data quality and consistency, ExampleValidator compares the statistics from the evaluation split to the schema generated from the training data. Any anomalies or deviations are detected, helping to identify potential issues early in the pipeline. This ensures that the data remains reliable and suitable for model training and evaluation.

validate_stats = ExampleValidator(
      statistics=statistics_gen.outputs['statistics'],
      schema=schema_gen.outputs['schema']
      )

Transform: Feature Engineering

The heart of feature engineering lies within the Transform component. Here, you define transformations using a preprocessing function (`preprocessing_fn`) that operates on the raw features. TensorFlow Transform (TFT) functions are at your disposal, allowing you to scale, bucketize, and apply various transformations to create meaningful features. These transformations are crucial for ensuring that the data is in a format suitable for model training.

transform = Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))

Putting It All Together: Creating a TFX Pipeline

Now, let’s put these components together to create a complete TFX pipeline for feature engineering. The pipeline ingests raw data, computes statistics, generates a schema, validates data quality, and performs feature engineering — all orchestrated seamlessly within TensorFlow TFX. This end-to-end approach ensures that your feature engineering process is efficient, reproducible, and production-ready.

Conclusion

Feature engineering is a critical step in the machine learning pipeline that directly influences model performance. TensorFlow TFX provides a robust framework for managing the feature engineering process, from data ingestion to transformation. By leveraging components like ExampleGen, StatisticsGen, SchemaGen, ExampleValidator, and Transform, you can ensure that your data is processed effectively and your machine learning models achieve their full potential.

Explore the documentation and start building efficient and powerful ML pipelines!

References

- TensorFlow TFX Documentation https://www.tensorflow.org/tfx/guide
- TensorFlow Data Validation Documentation-https://www.tensorflow.org/tfx/data_validation/get_started