New Book: Enterprise AI – An Applications Perspective

Author: Vincent Granville

Now published. Enterprise AI: An applications perspective takes a use case driven approach to understand the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a practical and straightforward roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk’s new course on Implementing Enterprise AI.

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Click here to get the book. For Data Science Central members only. If you have any issues accessing the book please contact us at info@datasciencecentral.com.

Contents

Introduction

Machine Learning, Deep Learning and AI
The Data Science Process
Categories of Machine Learning algorithms
How to learn rules from Data?
An introduction to Deep Learning
What problem does Deep Learning address?
How Deep Learning Drives Artificial Intelligence

Deep Learning and neural networks

Perceptrons – an artificial neuron
MLP – How do Neural networks make a prediction?
Spatial considerations – Convolutional Neural Networks
Temporal considerations – RNN/LSTM
The significance of Deep Learning
Deep Learning provides better representation for understanding the world
Deep Learning a Universal Function Approximator

What functionality does AI enable for the Enterprise?

Technical capabilities of AI
Functionality enabled by AI in the Enterprise value chain

Enterprise AI applications

Creating a business case for Enterprise AI
Four Quadrants of the Enterprise AI business case
Types of AI problems

Enterprise AI – Deployment considerations

A methodology to deploy AI applications in the Enterprise
DevOps and the CI/CD philosophy

Conclusions

DSC Resources

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