By Ajit Jaokar and Cheuk Ting Ho.
Exclusively for Data Science Central members, with free access. You can download this book (PDF) here.
Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a simple and practical 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.
After reading this book, the reader should be able to
- Understand Enterprise AI use cases
- De-mystify Enterprise AI
- Understand what problems Enterprise AI solves (and does not solve)
The term ‘Enterprise’ can be understood in terms of Enterprise workflows. These are already familiar through deployments of ERP systems like SAP or Oracle Financials. We consider both core Enterprise and the Wider enterprise workflows (including supply chain). The book is thus concerned with understanding the touchpoints which depict how Enterprise workflows could evolve when AI is deployed in the Enterprise.
The book comprises three sections
Section One: Enterprise AI – Concepts:
In this section, we explain the basic concepts of Machine Learning and Deep Learning and provide a brief landscape from an algorithmic perspective. We cover the basics of Machine Learning, Multilayer Perceptron, Auto encoders, Convolutional Neural Networks, Recurrent Neural Networks, Reinforcement learning, GANs etc.
Section Two: What functions can AI perform?
Independent of the Enterprise, in this section, we discuss what functions AI can perform through examples of companies performing these functions such as Recognition (Voice, Gestures, Gait, Handwriting, Objects, Scenes, Entities, faces, people) ; Generation (Generating text, Speech to text, Text to speech, Language translation, Speech imitation, Picture generation, Video generation, Image style transfer); Emotion state management (ex: attention tracking)
Section Three: Enterprise AI – Impact on process flows
AI impacts all areas of the Enterprise workflow. Examples include Sales and marketing (ex: Demand prediction); Customer support (ex: Cross-selling, Recommender and Personalization, Dynamic pricing); Procurement (ex: Picking Robots, Supply Chain Analytics); Production (ex: Quality control); Legal (ex: IP infringement); HR (ex: Recruiting Automation); Finance (ex: fraud detection)
Based in London, Ajit’s work spans research, entrepreneurship and academia relating to Artificial Intelligence (AI) and Internet of Things (IoT). Ajit works as a Data Scientist (Bioinformatics and IoT domains). He is the course director at Oxford University on “Data Science for Internet of Things”. Besides Oxford University, Ajit has also conducted AI courses in LSE, UPM and part of the Harvard Kennedy Future society research on AI.
Cheuk Ting Ho is a Data Scientist in Machine Learning and Deep Learning. She contributes regularly to the Data Science community by being public speaker, encouraging women in technology, and actively contributes to Python open source projects.
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