As enterprise adoption of AI and machine learning software becomes more commonplace, what does your company need to know to invest wisely in these technologies? In this detailed report, authors Rafael Coss, Dan Darnell, and Patrick Hall provide valuable information to help managers and practitioners make sound decisions for your organization in this commercial landscape.
Analytics adoption has driven a wave of digital transformation across industries, but many projects face significant drawbacks. Through the course of this report, you'll examine two of these issues: how the lack of involvement and access by domain experts and end users causes projects to lose focus and why predictive models often end up as services rather than part of new or existing applications.
The entire report covers a breadth of topics that include:
- The converging world of analytics: an up-to-date overview of the AI, ML, and analytics software ecosystem
- Modern AI applications: anatomy, key components, and detailed examples of the most promising use cases
- Adoption challenges for next-gen analytics: including organizational, infrastructure, modeling, governance, operational, and social issues
- Case studies: real-world perspectives from users of modern AI and ML software
- Title
- The Future of Analytics
- Subtitle
- The New Landscape of Artificial Intelligence and Machine Learning Applications
- Publisher
- O'Reilly Media
- Author(s)
- Dan Darnell, Patrick Hall, Rafael Coss
- Published
- 2020-10-16
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 48
- Language
- English
- ISBN-13
- 9781492091738
- License
- Compliments of H2O.ai
Copyright Table of Contents Introduction Chapter 1. The Converging World of Analytics Challenges in Current Analytics Projects From Business Intelligence to Augmented Analytics The Role of Automation in the Future of Analytics at Scale The AI App Revolution Current AI App Development Challenges The Impedance Mismatch Between AI and Existing Web Frameworks and Teams Generic AI Apps Noninteractive Experience Scarce Development Resources Chapter 2. Modern AI Applications The Anatomy of a Modern AI Application Key Components of an AI Application Platform Detailed Application Examples for Key Industries and Functions Mortgage Lending (Financial Services AI Applications) CPG Sales Forecasting with COVID-19 Data Hospital Staffing Optimization (Healthcare Industry AI Apps) Marketing Lead Optimization (Line of Business AI Applications) Data Augmentation (AI Apps for Data Teams) Chapter 3. Case Studies: Real Impacts of AI Application in Business ArmadaHealth Challenges Solution Results Hortifrut Challenges Solution Results Jewelers Mutual Challenges Solution Results Chapter 4. Adoption Challenges for Next-Generation Analytics Ineffective Data and AI Principles Lax Security Practices Inadequate Human Review Downplaying Traditional Domain Expertise AI Security and Privacy Conclusion About the Authors
