Reproducible Data Science with Pachyderm Learn how to build version-controlled, end-to-end data ...

Create scalable and reliable data pipelines easily with Pachyderm
Key Features
Learn how to build an enterprise-level reproducible data science platform with Pachyderm
Deploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and Microsoft Azure Kubernetes Service
Integrate Pachyderm with other data science tools, such as Pachyderm Notebooks
Book Description
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.
What you will learn
Understand the importance of reproducible data science for enterprise
Explore the basics of Pachyderm, such as commits and branches
Upload data to and from Pachyderm
Implement common pipeline operations in Pachyderm
Create a real-life example of hyperparameter tuning in Pachyderm
Combine Pachyderm with Pachyderm language clients in Python and Go
Who this book is for
This book is for new as well as experienced data scientists and machine learning engineers who want to build scalable infrastructures for their data science projects. Basic knowledge of Python programming and Kubernetes will be beneficial. Familiarity with Golang will be helpful.
Table of Contents
The Problem of Data Reproducibility
Pachyderm Basics
Pachyderm Pipeline Specification
Installing Pachyderm Locally
Installing Pachyderm on a Cloud Platform
Creating Your First Pipeline
Pachyderm Operations
Creating an End-to-End Machine Learning Workflow
Distributed Hyperparameter Tuning with Pachyderm
Pachyderm Language Clients
Using Pachyderm Notebooks