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The mountains of data generated on a daily basis has vastly outstripped growth in the supply of technical analysts. How can your organization provide value from these data volumes with your existing analytical capabilities and staff? With this report, business leaders looking to move from small AI pilot projects to an enterprise-wide deployment will explore an AI operations engineering framework known as AIOps.

Justin Neroda, Steve Escaravage, and Aaron Peters from Booz Allen Hamilton show you how to use this framework to develop AI tools, understand the importance of data management, and determine team roles and responsibilities. You'll learn how to unlock the incredible potential that lies within your organization's significantly growing data, deriving insights not obtainable just a few years ago.

  • Understand the challenges of moving AI deployments from a pilot project to production
  • Discover how an AIOps network can help you solve specific challenges on a case-by-case basis
  • Learn the building blocks required for enterprise-scale AI
  • Examine the principles of responsible AI and learn how to attain them
  • Establish clear objectives and measure performance for AI initiatives
  • Integrate different "Ops" methodologies, including DataOps and DevSecOps, to enable enterprise-wide AI solutions

Table of Contents

  1. Preface
    1. Acknowledgments
  2. 1. Demystifying AI
    1. AI Pilot-to-Production Challenges
    2. Scalability
    3. Sustainability
    4. Coordination
  3. 2. Defining the AIOps Framework
    1. Why You Need an AIOps Framework
    2. What Are the Benefits?
  4. 3. Responsible AI
    1. What Is Responsible AI?
    2. Adopting Responsible AI
    3. Ethics
    4. Workforce Development
    5. AI Risks and Complexities
    6. Risk Management Processes
  5. 4. Data: Your Most Precious Asset
    1. Data’s Role in AIOps
    2. Data Strategy
    3. DataOps: Operationalizing Your Data Strategy
    4. Data Preparation Activities
    5. Ingest
    6. Transformation
    7. Validation
    8. AI Data Governance
  6. 5. Machine Learning (ML)
    1. What Is an ML Model?
    2. ML Methodologies
    3. ML Advanced Topics
    4. ML Life Cycle
    5. Business Analysis
    6. Model Development
    7. Model Vetting
    8. Model Operation
    9. Machine Learning Operations (MLOps)
    10. Scalable Training Infrastructure
    11. Model Optimization Infrastructure
    12. Model Deployment Infrastructure
  7. 6. The Road to AI Adoption
    1. AI Adoption Blueprint
    2. Establishing Clear Objectives
    3. Measuring Outcomes and Performance
    4. Reference Architectures
    5. Technical Infrastructure
    6. Development Processes
    7. AIOps Integration
    8. Operational Components
    9. Component Integration
  8. Conclusion
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