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Reproducible Data Science with Pachyderm

You're reading from   Reproducible Data Science with Pachyderm Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801074483
Length 364 pages
Edition 1st Edition
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Author (1):
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Svetlana Karslioglu Svetlana Karslioglu
Author Profile Icon Svetlana Karslioglu
Svetlana Karslioglu
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction to Pachyderm and Reproducible Data Science
2. Chapter 1: The Problem of Data Reproducibility FREE CHAPTER 3. Chapter 2: Pachyderm Basics 4. Chapter 3: Pachyderm Pipeline Specification 5. Section 2:Getting Started with Pachyderm
6. Chapter 4: Installing Pachyderm Locally 7. Chapter 5: Installing Pachyderm on a Cloud Platform 8. Chapter 6: Creating Your First Pipeline 9. Chapter 7: Pachyderm Operations 10. Chapter 8: Creating an End-to-End Machine Learning Workflow 11. Chapter 9: Distributed Hyperparameter Tuning with Pachyderm 12. Section 3:Pachyderm Clients and Tools
13. Chapter 10: Pachyderm Language Clients 14. Chapter 11: Using Pachyderm Notebooks 15. Other Books You May Enjoy

Chapter 5: Installing Pachyderm on a Cloud Platform

In the previous chapter, you learned the process for installing Pachyderm locally to get started quickly and test Pachyderm on your computer.

Production use cases require additional compute resources and scalability that can be efficiently achieved using cloud platforms and managed Kubernetes platform services provided by the popular cloud vendors. Pachyderm can run on a Kubernetes cluster, irrespective of whether it is deployed manually on cloud instances or as a managed Kubernetes service. We will discuss the most popular and easy-to-configure methods on cloud providers.

This chapter walks you through the cloud-based installation of Pachyderm and explains the software requirements needed to run a Pachyderm cluster in production. This chapter will cover the installation on the following most popular cloud platforms: Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Microsoft Azure Kubernetes Service...

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