Achieving Real Business Outcomes from Artificial Intelligence

Book Description

Artificial intelligence is already changing industry landscapes, with early adopters reporting benefits in high-value business cases such as fraud detection, preventative maintenance, and recommendation engines. Yet working on an AI initiative is demanding for many enterprises, whether you’re in the middle of the process or just getting started. This ebook provides advice to help your company complete your AI journey.

Chad Meley from Teradata and Atif Kureishy and Ben Mackenzie from Think Big Analytics provide countermeasures for common AI challenges that arise when creating a strategy, dealing with technical issues, or operationalizing an AI initiative. You’ll explore several case studies, including how a major bank successfully used a variety of deep learning methods to fight financial crime.

With this ebook, you’ll discover:

  • How deep learning has the potential to increase production, drive down cost, reduce waste, improve efficiency, and push innovation
  • Options and trade-offs for leveraging AI capabilities, including SaaS solutions, public cloud-based APIs, and custom AI models
  • AI case studies for mining image data, using image recognition, providing customer service, and designing document automation
  • How to overcome challenges in delivering value from custom AI development
  • What to do in the face of emerging AI trends over the next three years

Table of Contents

  1. Foreword
  2. Acknowledgments
    1. Our Customers
    2. Our Talented Data Scientists
    3. Industry Analysts
    4. Our Community
  3. 1. Artificial Intelligence and Our World
    1. A New Age of Computation
    2. The AI Trinity: Data, Hardware, and Algorithms
      1. Exponential Growth of Data
      2. Computational Advances to Handle Big Data
      3. Accessing and Developing Algorithms
    3. What Is AI: Deep Versus Machine Learning
    4. What Is Deep Learning?
    5. Why It Matters
  4. 2. More Than Games and Moonshots
    1. AI-First Strategy
    2. Where Deep Learning Excels
    3. Financial Crimes
    4. Manufacturing Performance Optimization
    5. Recommendation Engines
    6. Yield Optimization
    7. Predictive Maintenance
  5. 3. Options and Trade-Offs for Enterprises to Consume Artificial Intelligence
    1. SaaS Solutions: Quick but Limited
    2. Cloud AI APIs
    3. Building Custom AI Algorithms
  6. 4. Challenges to Delivering Value from Custom AI Development and Engineering Countermeasures
    1. Strategy
    2. Technology
    3. Operations
      1. AnalyticOps
      2. Model Transparency
      3. The Move to Autonomous Decisions
    4. Data
    5. Talent
    6. Conclusion
  7. 5. Artificial Intelligence Case Studies
    1. Fighting Fraud by Using Deep Learning
    2. Mining Image Data to Increase Productivity
    3. Deep Learning for Image Recognition
    4. Natural-Language Processing for Customer Service
    5. Deep Learning for Document Automation
    6. Conclusion
  8. 6. Danske Bank Case Study Details
    1. The Project, the Tools, and the Team
    2. Getting the Right Data in Place
    3. Ensemble Modeling and Champion/Challenger
    4. Working with the Past, Building the Future
    5. Moving the ML Models into Live Production
    6. From Machine Learning to Deep Learning
    7. Visualizing Fraud
    8. Visualizing and Interpreting Deep Learning Models
    9. A Platform for the Future
  9. 7. Predictions Through 2020
    1. Strategy
    2. Technology
    3. Operations
    4. Data
    5. Talent
    6. What’s Next
  10. 8. Conclusion
    1. Identify High-Impact Business Outcomes
    2. Assess Current Capabilities
    3. Build Out Capabilities

Product Information

  • Title: Achieving Real Business Outcomes from Artificial Intelligence
  • Author(s): Atif Kureishy, Chad Meley, Ben Mackenzie
  • Release date: November 2018
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492038207