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Intelligent Workloads at the Edge

You're reading from   Intelligent Workloads at the Edge Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811781
Length 374 pages
Edition 1st Edition
Tools
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Authors (2):
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Ryan Burke Ryan Burke
Author Profile Icon Ryan Burke
Ryan Burke
Indraneel (Neel) Mitra Indraneel (Neel) Mitra
Author Profile Icon Indraneel (Neel) Mitra
Indraneel (Neel) Mitra
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction and Prerequisites
2. Chapter 1: Introduction to the Data-Driven Edge with Machine Learning FREE CHAPTER 3. Section 2: Building Blocks
4. Chapter 2: Foundations of Edge Workloads 5. Chapter 3: Building the Edge 6. Chapter 4: Extending the Cloud to the Edge 7. Chapter 5: Ingesting and Streaming Data from the Edge 8. Chapter 6: Processing and Consuming Data on the Cloud 9. Chapter 7: Machine Learning Workloads at the Edge 10. Section 3: Scaling It Up
11. Chapter 8: DevOps and MLOps for the Edge 12. Chapter 9: Fleet Management at Scale 13. Section 4: Bring It All Together
14. Chapter 10: Reviewing the Solution with AWS Well-Architected Framework 15. Other Books You May Enjoy Appendix 1 – Answer Key

Designing an ML workflow in the cloud

ML is an end-to-end (E2E) iterative process consisting of multiple phases. As we explain the different phases throughout the rest of the book, we will align to the general guidelines provided by Cross Industry Standard Process for Data Mining (CRISP-DM) consortium. The CRISP-DM reference model was conceived in late 1996 by three pioneers of the emerging data mining market and continued to evolve through participation from multiple organizations and service suppliers across various industry segments. The following diagram shows the different phases of the CRISP-DM reference model:

Figure 7.8 – Phases of the CRISP-DM reference model (redrawn from https://www.the-modeling-agency.com/crisp-dm.pdf)

This model is still considered a baseline and a proven tool for conducting successful data mining projects as its application is neutral and applies well to a wide variety of ML pipelines and workloads. Using the preceding...

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