Book Image

Reproducible Data Science with Pachyderm

By : Svetlana Karslioglu
Book Image

Reproducible Data Science with Pachyderm

By: Svetlana Karslioglu

Overview of this book

Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale. You’ll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you’ll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You’ll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you’ll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks. By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis.
Table of Contents (16 chapters)
1
Section 1: Introduction to Pachyderm and Reproducible Data Science
5
Section 2:Getting Started with Pachyderm
12
Section 3:Pachyderm Clients and Tools

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

image = displacy.render(textfile, style='dep', options={"compact": True, "distance": 70})
 f = open('/pfs/out/pos-tag-dependency.svg', "w")
 f.write(image)
 f.close()

Any command-line input or output is written as follows:

$ minikube start

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Select System info from the Administration panel."

Tips or Important notes

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