Databricks is a unified big data and analytics platform. It is great for training ML models and working with the kind of large-scale data that is often found in IoT. There are extensions such as Delta Lake that allow researchers the ability to view data as it existed at certain periods of time so that they can do analysis when models drift. There are also tools such as MLflow that allow the data scientist to compare multiple models against each other. In this recipe, we are going to install various ML packages such as TensorFlow, PyTorch, and GraphFrames on Databricks. Most ML packages can be installed via PyPI. The format used to install TensorFlow, for example, will work on various ML frameworks such as OpenAI Gym, Sonnet, Keras, and MXNet. Some tools are available in Databricks that are not available in Python. For those, we use the pattern explored by GraphX and GraphFrame where packages are installed through Java extensions.
Artificial Intelligence for IoT Cookbook
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Artificial Intelligence for IoT Cookbook
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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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