Prompt: How can I orchestrate Apache Beam pipelines with TFX?

Response: You can use Apache Beam for various tasks within your machine learning project, including data validation, preprocessing, model validation, and model deployment and inference.

However, a comprehensive ML workflow also involves additional steps like data exploration, feature engineering, and model training. Additionally, ML workflows require metadata and artifact tracking for reproducibility and auditability. Popular tools for building and managing ML workflows include Kubeflow, TFX, and Apache Airflow.

TFX is an end-to-end platform designed for deploying production ML pipelines. TFX pipelines consist of a sequence of components that together form an ML pipeline. Each TFX pipeline DAG requires its own orchestrator. The natively supported orchestrators include Kubeflow Pipelines, Apache Airflow, and Apache Beam itself.

To integrate an Apache Beam pipeline into a TFX pipeline, you need to follow these steps:

1. Create TFX components by specifying interfaces and containerizing the component implementation.
2. Create the TFX pipeline by connecting these components and specifying how inputs and outputs are exchanged between them.
3. Compile and execute the TFX pipeline in the target environment.

TFX provides a rich set of standard components for constructing ML pipelines. These components can be used as building blocks for creating custom components. TFX heavily relies on Apache Beam libraries for implementing data-parallel pipelines within these components. You must execute components created using these libraries with one of the supported Apache Beam runners.

To launch the pipeline, you must provide two configurations: the orchestrator for the TFX pipeline and the pipeline options necessary for running Apache Beam pipelines.

You can find the complete TFX example code in the Apache Beam GitHub repository.