For this experiment, you will need IoT Hub. Next, you'll need to create a streaming analytics job. To do this, you will go into the Azure portal and create a new streaming analytics job through the Create new resource wizard. After you create a new streaming analytics job, you will see that there are three main components on the Overview page. These are inputs, outputs, and queries. Inputs, as the name suggests, are the streams you want to input; in our case, we are inputting IoT Hub. To connect to IoT Hub you need to click on Inputs, then select the input type of IoT Hub, and then select the IoT Hub instance you created for this recipe. Next, you can create an output. This could be a database such as Cosmos DB or a function app so that you can send alerts through any number of messaging systems. For the sake of simplicity, we are not going to specify output for this recipe. For testing purposes, you can review the output on the Stream Analytics query editor.
Artificial Intelligence for IoT Cookbook
By :
Artificial Intelligence for IoT Cookbook
By:
Overview of this book
Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.
Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease.
By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)
Preface
Setting Up the IoT and AI Environment
Free Chapter
Handling Data
Machine Learning for IoT
Deep Learning for Predictive Maintenance
Anomaly Detection
Computer Vision
NLP and Bots for Self-Ordering Kiosks
Optimizing with Microcontrollers and Pipelines
Deploying to the Edge
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