Book Image

Hands-On Industrial Internet of Things

By : Giacomo Veneri, Antonio Capasso
Book Image

Hands-On Industrial Internet of Things

By: Giacomo Veneri, Antonio Capasso

Overview of this book

We live in an era where advanced automation is used to achieve accurate results. To set up an automation environment, you need to first configure a network that can be accessed anywhere and by any device. This book is a practical guide that helps you discover the technologies and use cases for Industrial Internet of Things (IIOT). Hands-On Industrial Internet of Things takes you through the implementation of industrial processes and specialized control devices and protocols. You’ll study the process of identifying and connecting to different industrial data sources gathered from different sensors. Furthermore, you’ll be able to connect these sensors to cloud network, such as AWS IoT, Azure IoT, Google IoT, and OEM IoT platforms, and extract data from the cloud to your devices. As you progress through the chapters, you’ll gain hands-on experience in using open source Node-Red, Kafka, Cassandra, and Python. You will also learn how to develop streaming and batch-based Machine Learning algorithms. By the end of this book, you will have mastered the features of Industry 4.0 and be able to build stronger, faster, and more reliable IoT infrastructure in your Industry.
Table of Contents (18 chapters)

Questions

  1. What is a Jupyter Notebook?
    1. An IDE in which to develop Azure ML or SageMaker analytics
    2. A general-purpose interactive IDE for Python and other languages
    3. A notebook used by NASA
  2. Which one of the following steps is more appropriate to deploy analytics on Azure ML, SageMaker, or GCP Analytics?
    1. Prepare the data, train the model, test the model, deploy the model
    2. Prepare the data, build the model, deploy the model, monitor the model
    3. Prepare the data, build the model, (train the model, test the model), deploy the model, monitor the model
  3. What is the basic idea of SageMaker and Azure ML?
    1. To build the model as a web application on a containerized application
    2. To learn the model using FPGA and GPU
    3. To allocate a computational cluster in which to test the analytics