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Google announces beta launch of Cloud AI Platform pipelines


Google Cloud Platform (GCP) recently announced the beta launch of Cloud AI Platform Pipelines, a new product to automate and manage machine learning (ML) workflows, which exploits open source technologies TensorFlow Extended (TFX) and Kubeflow pipelines (KFP).

In a recent blog post, product manager Anusha Ramesh and developer advocate Amy Unruh shared overview of the offer and its characteristics. Cloud AI Platform Pipelines solves the problem of managing end-to-end ML workflows, which cover the life cycle of raw data ingestion, training and model evaluation, including inference of models in production. New Product Contains Tools to Create Workflows, Track Artifacts and Workflow Lineage, and “Enterprise Ready” Workflow Execution Infrastructure that Integrates with Other Services GCP such as BigQuery and Data flow. According to Ramesh and Unruh,

Cloud AI Platform Pipelines provides a means to deploy robust and reproducible machine learning pipelines as well as monitoring, auditing, versioning and reproducibility, and provides a secure, easy-to-install and ready-to-run execution environment the business for your ML workflows.

Cloud AI Platform Pipelines is a managed implementation of TensorFlow Extended (TFX) and Kubeflow Pipelines (KFP) that runs on a Google Kubernetes Engine (GKE) cluster. TFX is an abstraction layer in which the basic concept is a pipeline, a series of data transformation steps (pipeline components) which must be coordinated or orchestra; the data transferred between the components is called artifacts. KFP is the orchestrator, which runs each component of the pipeline on a pod in the GKE cluster. TFX also defines a database for ML metadata (MLMD), which tracks the history and versions of a pipeline as well as the artifacts produced by it. Cloud AI Platform Pipelines supports two SDKs, the higher-level TFX SDK and the lower-level KFP SDK; however, Google plans to merge the two into a single TFX SDK.


TFX was the first described by Google in an article presented to KDD 2017, documenting the results of Google’s efforts to create an end-to-end ML platform that included all phases of the ML process, including data analysis and transformation, model training and evaluation, and inference in production. The original execution infrastructure was Apache Beam, which was itself based on Nozzle and now powers Google Cloud Dataflow. TFX still uses Beam to define parallel data operations, but now also supports Kubeflow and Apache Airflow as orchestration engines. Airflow is the technology behind another GCP product, Cloud Composer.

Airflow and Cloud Composer are general purpose workflow orchestration technologies and have been recommended by Google in the past for management of ML workflows. In 2018 Google open-source Kubeflow as a specific ML platform targeted for Kubernetes; Spotify recently adopted it as a standard ML platform and opened its Terraform models to create clusters. Google’s new Cloud AI Platform Pipelines offer further sums up the work by managing the GKE cluster. In a discussion on Hacker News, a user noted:

The battle for ML pipeline ecosystems is the engine – not so much the API. It was Beam vs [Apache] Spark. Now Google changes course and says it’s TensorFlow on Kubernetes with distributed processing compared to ML pipelines based on Spark.

Source code for both Tensorflow Extended and Kubeflow pipelines are available on GitHub.

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