Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael Roshak

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)

Installing GraphX and GraphFrames

Spark has some distributed libraries that are not available anywhere else in data science. GraphFrames is one of them. In graph theory, you can perform actions such as finding the shortest path, network flow, homophily, centrality, and influence. Because GraphFrames is built on GraphX, which is a Java library, you need to install the Java library, and then to use the Python wrapper, you will need to pip install the Python library that accesses the Java JAR file. The installation steps are as follows:

  1. Download a JAR file from https://spark-packages.org/package/graphframes/graphframes. You'll need to find a version that matches the version of Spark that you are running in your cluster.
  2. In the Workspace tab of Databricks, right-click anywhere and from the dropdown, click on Create and then Library.
  3. Drag and drop the JAR file into the space titled Drop JAR here.
  4. Click Create.
  5. Then, import another library.
  6. In the Workspace tab of Databricks...