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

Chapter 7

  1. False – there are more than two types of ML systems.
  2. The four types of ML systems are supervised, unsupervised, semi-supervised, and reinforcement learning. They range in utility based on how much we know about the input data, and whether we are training for a specific result or looking to see what the algorithm can find in the noise.
  3. False – K-means is a clustering algorithm.
  4. The three phases of the ML project lifecycle are data collection, data preparation, and modeling.
  5. Two frameworks for training ML models are TensorFlow and MXNet.
  6. AWS IoT Greengrass is the tool to deploy trained models from the cloud to the edge.
  7. False – Greengrass offers managed components ready for image classification.
  8. One anti-pattern for ML and IoT workloads is setting an expectation that a single person has the expertise for data preparation, ML training, and deploying solutions to the edge.