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Apache Beam is a key technology for building scalable End-to-End ML pipelines, as it is the data preparation and model analysis engine for TensorFlow Extended (TFX), a framework for horizontally scalable Machine Learning (ML) pipelines based on TensorFlow. In this talk, we present TFX on Hopsworks, a fully open-source platform for running TFX pipelines on any cloud or on-premise. Hopsworks is a project-based multi-tenant platform for both data parallel programming and horizontally scalable machine learning pipelines. Hopsworks supports Apache Flink as a runner for Beam jobs and TFX pipelines are supported through Airflow support in Hopsworks. We will demonstrate how to build a ML pipeline with TFX, Beam’s Python API and the Flink Runner by using Jupyter notebooks, explain how security is transparently enabled with short-lived TLS certificates, and go through all the pipeline steps, from Data Validation, to Transformation, Model training with TensorFlow, Model Analysis, Model Serving and Monitoring with Kubernetes.
To the best of our knowledge, Hopsworks is the first fully open-source on-premise platform that supports both TFX pipelines and Apache Beam.