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

MLOps at the edge

Machine Learning Operations (MLOps) aims to integrate agile methodologies into the end-to-end process of running machine learning workloads. MLOps brings together best practices from data science, data engineering, and DevOps to streamline model design, development, and delivery across the machine learning development life cycle (MLDLC).

As per MLOps special interest group (SIG), MLOps is defined as "The extension of the DevOps methodology to include machine learning and data science assets as first-class citizens within the DevOps ecology." MLOps has gained rapid momentum in the last few years from ML practitioners and is a language-, framework-, platform-, and infrastructure-agnostic practice.

The following diagram shows the virtuous cycle of the MLDLC:

Figure 8.11 – MLOps workflow

The preceding diagram shows how Operations is a fundamental block of the ML workflow. We introduced some of the concepts of ML design...