Book Image

Intelligent Workloads at the Edge

By : Indraneel Mitra, Ryan Burke
Book Image

Intelligent Workloads at the Edge

By: Indraneel Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

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...