March 2022
Intermediate to advanced
364 pages
6h 44m
English
Overview
Dive into the world of reproducible data science with Pachyderm, a specialized platform designed for version-controlled data pipelines. By following this book, 'Reproducible Data Science with Pachyderm,' you'll gain the skills to implement robust, scalable machine learning workflows with Pachyderm 2.0, covering setup, integration, and advanced use cases.
What this Book will help me do
- Build scalable, version-controlled data pipelines with Pachyderm's unique features.
- Understand the principles behind reproducible data science and implement them effectively.
- Deploy Pachyderm on AWS, Google Cloud, and Azure while integrating with popular tools.
- Create and manage end-to-end machine learning workflows, including hyperparameter tuning.
- Leverage advanced integrations, such as Pachyderm Notebooks and language clients like Python and Go.
Author(s)
Svetlana Karslioglu is a seasoned data scientist with extensive experience in constructing scalable machine learning and data processing systems. With years in both practical implementation and educational endeavors, she has a talent for breaking down complex concepts into accessible learning paths. Her approach is hands-on and results-oriented, aimed at empowering professionals to excel in the field of data science.
Who is it for?
This book is intended for data scientists, machine learning engineers, and data engineers who are keen to ensure reproducibility in their workflows. Ideal readers may have familiarity with data science basics and some exposure to Kubernetes and programming languages like Python. By studying the book, learners will establish confidence in implementing Pachyderm for scalable and reliable data pipelines.
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