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Machine Learning Engineering with MLflow

You're reading from   Machine Learning Engineering with MLflow Manage the end-to-end machine learning life cycle with MLflow

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800560796
Length 248 pages
Edition 1st Edition
Tools
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Author (1):
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Natu Lauchande Natu Lauchande
Author Profile Icon Natu Lauchande
Natu Lauchande
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Problem Framing and Introductions
2. Chapter 1: Introducing MLflow FREE CHAPTER 3. Chapter 2: Your Machine Learning Project 4. Section 2: Model Development and Experimentation
5. Chapter 3: Your Data Science Workbench 6. Chapter 4: Experiment Management in MLflow 7. Chapter 5: Managing Models with MLflow 8. Section 3: Machine Learning in Production
9. Chapter 6: Introducing ML Systems Architecture 10. Chapter 7: Data and Feature Management 11. Chapter 8: Training Models with MLflow 12. Chapter 9: Deployment and Inference with MLflow 13. Section 4: Advanced Topics
14. Chapter 10: Scaling Up Your Machine Learning Workflow 15. Chapter 11: Performance Monitoring 16. Chapter 12: Advanced Topics with MLflow 17. Other Books You May Enjoy

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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