Introduction to Machine Learning Operations (MLOps) with TFX and Kubeflow

Introduction to Machine Learning Operations (MLOps) with TFX and Kubeflow

In this post we cover some introductory tutorials on Machine Learning Operations (MLOps), a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.

In particular, we take a look at the following technologies:

Netflix

  • TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you’re ready to move your models to production, use TFX to create and manage a production pipeline.

Netflix

  • The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow pipelines is a component of the Kubeflow project which can be used in combination with TFX.

Overview about how to use Tensor Flow Extended (TFX) with Kubeflow pipelines:

MLOps - Build pipelines with Tensor Flow Extended & Kubeflow von Jan Kirenz


Tutorials:

Tensor Flow Extended (TFX)

Kubeflow

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Jan Kirenz
Professor

I’m a data scientist educator and consultant.