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.

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Spis treści

Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0 eBook -- spis treści

• 1. The Problem of Data Reproducibility
• 2. Pachyderm Basics 
• 3. Pachyderm Pipeline Specification
• 4. Installing Pachyderm Locally
• 5. Installing Pachyderm on a Cloud Platform
• 6. Creating Your First Pipeline
• 7. Pachyderm Operations
• 8. Creating an End-to-End Machine Learning Workflow 
• 9. Distributed Hyperparameter Tuning with Pachyderm
• 10. Pachyderm Language Clients
• 11. Using Pachyderm Notebooks