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Machine Learning on Kubernetes

You're reading from   Machine Learning on Kubernetes A practical handbook for building and using a complete open source machine learning platform on Kubernetes

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781803241807
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
2. Chapter 1: Challenges in Machine Learning FREE CHAPTER 3. Chapter 2: Understanding MLOps 4. Chapter 3: Exploring Kubernetes 5. Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes
6. Chapter 4: The Anatomy of a Machine Learning Platform 7. Chapter 5: Data Engineering 8. Chapter 6: Machine Learning Engineering 9. Chapter 7: Model Deployment and Automation 10. Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform
11. Chapter 8: Building a Complete ML Project Using the Platform 12. Chapter 9: Building Your Data Pipeline 13. Chapter 10: Building, Deploying, and Monitoring Your Model 14. Chapter 11: Machine Learning on Kubernetes 15. Other Books You May Enjoy

Chapter 5: Data Engineering

Data engineering, in general, refers to the management and organization of data and data flows across an organization. It involves data gathering, processing, versioning, data governance, and analytics. It is a huge topic that revolves around the development and maintenance of data processing platforms, data lakes, data marts, data warehouses, and data streams. It is an important practice that contributes to the success of big data and machine learning (ML) projects. In this chapter, you will learn about the ML-specific topics of data engineering.

A sizable number of ML tutorials/books start with a clean dataset and a CSV file to build your model against. The real world is different. Data comes in many shapes and sizes, and it is important that you have a well-defined strategy to harvest, process, and prepare data at scale. This chapter will discuss open source tools that can provide the foundations for data engineering in ML projects. You will learn...

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