Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build | C2C Community

Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build

  • 18 August 2022
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MLOps, like DevOps, is used to enhance the quality and minimise the time to market of machine learning engineering. It may improve team cooperation, increase the reliability and scalability of ML systems, and minimise development cycle times.It can keep the ML system from failing due to a failure to respond to environmental changes.

 

Here is a detailed look at the finest cloud documentation-based MLOPs implementational architecture.This article demonstrates, using real-world examples, how you can practise MLOps and acquire ideas for constructing valuable solution infrastructure.

this document covers the following topics:

  • Understanding CI/CD and automation in ML.
  • Designing an integrated ML pipeline with TFX.
  • Orchestrating and automating the ML pipeline using Kubeflow Pipelines.
  • Setting up a CI/CD system for the ML pipeline using Cloud Build.

 

Please click on the following link to read more details:


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