Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Understanding stream processing


The software applications that are deployed in the enterprise have two basic components:

  • The infrastructure
  • The applications

The infrastructure includes the physical hardware and the network that connects different systems together. The security implementation for infrastructure and applications have different considerations due to which the frameworks and processes for protecting the CI are also different. 

The security systems need to operate across the peripheries of the infrastructure and within the applications. There are various events through which the data (network and application) flows. The events take place at a point in time and the corresponding data is available for analysis and action immediately after the event occurs.

For example, a client application such as a web browser requests access to a website over the HTTP protocol. The sequence of events are initiated right after the URL is entered through the browser. The related analysis based on the...