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You're reading from  Machine Learning Engineering with MLflow

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Published inAug 2021
PublisherPackt
ISBN-139781800560796
Edition1st Edition
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Natu Lauchande
Natu Lauchande
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Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
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Deploying your models for batch scoring in Kubernetes

We will use Kubernetes to deploy our batch scoring job. We will need to do some modifications to make it conform to the Docker format acceptable to the MLflow deployment in production through Kubernetes. The prerequisite of this section is that you have access to a Kubernetes cluster or can set up a local one. Guides for this can be found at https://kind.sigs.k8s.io/docs/user/quick-start/ or https://minikube.sigs.k8s.io/docs/start/.

You will now execute the following steps to deploy your model from the registry in Kubernetes:

  1. Prerequisite: Deploy and configure kubectl (https://kubernetes.io/docs/reference/kubectl/overview/) and link it to your Kubernetes cluster.
  2. Create a Kubernetes backend configuration file:
    {
      "kube-context": "docker-for-desktop",
      "repository-uri": "username/mlflow-kubernetes-example",
      "kube-job-template-path"...
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Machine Learning Engineering with MLflow
Published in: Aug 2021Publisher: PacktISBN-13: 9781800560796

Author (1)

author image
Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
Read more about Natu Lauchande