Preprocess data with MLTransform

This page explains how to use the MLTransform class to preprocess data for machine learning (ML) workflows. Apache Beam provides a set of data processing transforms for preprocessing data for training and inference. The MLTransform class wraps the various transforms in one class, simplifying your workflow. For a full list of available transforms, see the Transforms section on this page.

Why use MLTransform

Support and limitations


You can use MLTransform to generate text embeddings and to perform various data processing transforms.

Text embedding transforms

You can use MLTranform to generate embeddings that you can use to push data into vector databases or to run inference.

Transform nameDescription
SentenceTransformerEmbeddingsUses the Hugging Face sentence-transformers models to generate text embeddings.
VertexAITextEmbeddingsUses models from the the Vertex AI text-embeddings API to generate text embeddings.

Data processing transforms that use TFT

The following set of transforms available in the MLTransform class come from the TensorFlow Transforms (TFT) library. TFT offers specialized processing modules for machine learning tasks. For information about these transforms, see Module:tft in the TensorFlow documentation.

Transform nameDescription
ApplyBucketsSee tft.apply_buckets in the TensorFlow documentation.
BucketizeSee tft.bucketize in the TensorFlow documentation.
ComputeAndApplyVocabularySee tft.compute_and_apply_vocabulary in the TensorFlow documentation.
NGramsSee tft.ngrams in the TensorFlow documentation.
ScaleByMinMaxSee tft.scale_by_min_max in the TensorFlow documentation.
ScaleTo01See tft.scale_to_0_1 in the TensorFlow documentation.
ScaleToZScoreSee tft.scale_to_z_score in the TensorFlow documentation.
TFIDFSee tft.tfidf in the TensorFlow documentation.

I/O requirements


Artifacts are additional data elements created by data transformations. Examples of artifacts are the minimum and maximum values from a ScaleTo01 transformation, or the mean and variance from a ScaleToZScore transformation.

In the MLTransform class, the write_artifact_location and the read_artifact_location parameters determine whether the MLTransform class creates artifacts or retrieves artifacts.

Write mode

When you use the write_artifact_location parameter, the MLTransform class runs the specified transformations on the dataset and then creates artifacts from these transformations. The artifacts are stored in the location that you specify in the write_artifact_location parameter.

Write mode is useful when you want to store the results of your transformations for future use. For example, if you apply the same transformations on a different dataset, use write mode to ensure that the transformation parameters remain consistent.

The following examples demonstrate how write mode works.

Read mode

When you use the read_artifact_location parameter, the MLTransform class expects the artifacts to exist in the value provided in the read_artifact_location parameter. In this mode, MLTransform retrieves the artifacts and uses them in the transform. Because the transformations are stored in the artifacts when you use read mode, you don’t need to specify the transformations.

Artifact workflow

The following scenario provides an example use case for artifacts.

Before training a machine learning model, you use MLTransform with the write_artifact_location parameter. When you run MLTransform, it applies transformations that preprocess the dataset. The transformation produces artifacts that are stored in the location specified by the write_artifact_location parameter value.

After preprocessing, you use the transformed data to train the machine learning model.

After training, you run inference. You use new test data and use the read_artifact_location parameter. By using this setting, you ensure that the test data undergoes the same preprocessing steps as the training data. In read mode, running MLTransform fetches the transformation artifacts from the location specified in the read_artifact_location parameter value. MLTransform applies these artifacts to the test data.

This workflow provides consistency in preprocessing steps for both training and test data. This consistency ensures that the model can accurately evaluate the test data and maintain the integrity of the model’s performance.

Preprocess data with MLTransform

To use the MLTransform transform to preprocess data, add the following code to your pipeline:

  import apache_beam as beam
  from import MLTransform
  from import <TRANSFORM_NAME>
  import tempfile

  data = [

  artifact_location = tempfile.mkdtemp()

  with beam.Pipeline() as p:
    transformed_data = (
        | beam.Create(data)
        | MLTransform(write_artifact_location=artifact_location).with_transform(
        | beam.Map(print))

Replace the following values:

For more examples, see MLTransform for data processing in the transform catalog.