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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

Summary

In this chapter, we have learned how to create Pachyderm notebooks in Pachyderm Hub, a powerful addition to Pachyderm that enables data scientists to leverage the benefits of an integrated environment with the Pachyderm data lineage functionality and pipelines. Data scientists spend hours performing exploratory data analysis and do so in notebooks. Combining Pachyderm and notebooks brings data scientists and data engineers together on one platform, letting them speak the same language and use the same tools.

In addition to the above, we created a pipeline that performs basic sentiment analysis of Twitter data and ran it completely in a Pachyderm notebook. We have expanded our knowledge of Python Pachyderm and how it can be used in conjunction with other tools and libraries.

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