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

Data visualization and analytics

In this phase, you can continue the data exploration through various analytics and visualization tools to assess the data fitment for ML training post profiling. You can continue to leverage services such as Amazon Athena, Amazon Quicksight, and others introduced to you in Chapter 6, Processing and Consuming Data on the Cloud.

Feature engineering (FE)

In this phase, your responsibilities as IoT professionals are very limited. This is where the data scientists will determine the unique attributes in the dataset that can be useful in training the ML model. You can think of rows as observations and columns as properties (or attributes). As data scientists, your goal is to identify the columns that matter in solving a specific business problem (aka features). For example, with image classification, the color or brand of a car is not a key feature to determine it as a vehicle. This process of selecting and transforming variables to ensure the creation...