Get Started with ML (original) (raw)

Get started with AI/ML pipelines

Being productive and successful as a machine learning practitioner is often dependent on your ability to efficiently leverage large volumes of data in a way that is uniquely tailored to your resources, requirements, and budget. Whether starting your next AI/ML project or upscaling an existing project, consider adding Apache Beam to your project.

Use Beam ML

I want use Beam ML to do:

• Prediction and inference
• Data processing
• Workflow orchestration
• Model training
• Anomaly detection

Prediction and inference

Beam provides different ways to implement inference as part of your pipeline. You can run your ML model directly in your pipeline and apply it on big scale datasets, both in batch and streaming pipelines.

RunInference

The RunInference API is available with the Beam Python SDK versions 2.40.0 and later. You can use Apache Beam with the RunInference API to use machine learning (ML) models to do local and remote inference with batch and streaming pipelines. Starting with Apache Beam 2.40.0, PyTorch and Scikit-learn frameworks are supported. Tensorflow models are supported through tfx-bsl. For more deatils about using RunInference, see About Beam ML.

The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam’s Multi-language Pipelines framework. For information about the Java wrapper transform, see RunInference.java. To try it out, see the Java Sklearn Mnist Classification example.

You can create multiple types of transforms using the RunInference API: the API takes multiple types of setup parameters from model handlers, and the parameter type determines the model implementation.

Prediction and inference examples

Data processing

You can use Apache Beam for data validation and preprocessing by setting up data pipelines that transform your data and output metrics computed from your data. Beam has a rich set of I/O connectors for ingesting and writing data, which allows you to integrate it with your existing file system, database, or messaging queue.

Workflow orchestration

In order to automate and track the AI/ML workflows throughout your project, you can use orchestrators such as Kubeflow pipelines (KFP) or TensorFlow Extended (TFX). These orchestrators automate your different building blocks and handle the transitions between them.

When you use Apache Beam as one of the building blocks in your project, these orchestrators are able to launch your Apache Beam job and to keep track of the input and output of your pipeline. These tasks are essential when moving your AI/ML solution into production, because they allow you to handle your model and data over time and improve the quality and reproducibility of results.

Model training

Use cases

Reference

• RunInference metrics
• ML model evaluation
• RunInference public codelab
• RunInference notebooks
• Beam ML dependency management

Last updated on 2026/06/17

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