Dataflow ML

Dataflow ML lets you use Dataflow to deploy and manage complete machine learning (ML) pipelines. Use ML models to do local and remote inference with batch and streaming pipelines. Use data processing tools to prepare your data for model training and to process the results of the models.
About Dataflow ML
Whether you want to classify images in real-time, run remote inference calls, or build a custom model handler, you can find complete Dataflow ML examples.
See use cases
Use the MLTransform class to preprocess data for machine learning (ML) workflows. By combining multiple data processing transforms in one class, MLTransform streamlines the process of applying Apache Beam ML data processing transforms to your workflow.
Learn more 
with pipeline as p:
  predictions = (
      p
      | beam.ReadFromSource('a_source')
      | RunInference(MODEL_HANDLER))
Using RunInference is as straightforward as adding the transform code to your pipeline. In this example, MODEL_HANDLER is the model configuration object. 
with beam.Pipeline() as p:
  transformed_data = (
    p
    | beam.Create(data)
    | MLTransform(...)
    | beam.Map(print))
To prepare your data for training ML models, use MLTransform in your pipeline. MLTransform wraps multiple data processing transforms in one class, letting you use one class for a variety of preprocessing tasks.

Prediction and inference with pre-trained models

Use a pre-trained model with Pytorch.
Use a pre-trained model with scikit-learn.
Use a pre-trained model with TensorFlow.
Apache Beam has built-in support for sending requests to a remotely deployed Agent Platform endpoint. This notebook shows how to use the Apache Beam RunInference transform for image classification with Agent Platform.
Use the RunInference transform with a keyed model handler to use multiple models in the same RunInference transform.

Data processing with MLTransform