Managing AI Projects. Drive Innovation and Successfully Navigate the Full AI Project Lifecycle Malini Jain Runtasewee, Adrián González Sánchez

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Spis treści książki

• Preface: Our AI Project Management Dream
  • Who This Book Is For
  • How This Book Is Organized
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to AI Project Management
  • Levels of AI Management
    • Level 1: AI Strategy
      • Business strategy
      • Technology and data strategy
      • AI expertise and development strategy
      • Organizational culture
      • Internal AI governance
    • Level 2: Tactical Management
    • Level 3: Technical Management
      • Defining the technology stack
      • Managing and measuring technical AI performance
  • Demystifying the AI Project Manager Role
    • Fact 1: There Is No One Way to Manage AI Projects
    • Fact 2: Managing AI Projects and AI for Project Managers Are Not the Same
      • Backlog enrichment
      • Preassignment of user stories
      • Roadmap ideation
      • Generation of additional documentation
      • Creation of end-of-week or end-of-sprint email drafts
      • Internal research for technical topics
      • Creation of compliance documentation
    • Fact 3: AI-Forward Companies Expect Certain Skills from You
      • Skills you will need to demonstrate
      • Framework for AI project managers
    • Fact 4: Success Is All About Knowing the Differences Between Regular and AI Project Management
  • Main Challenges for AI Project Management Professionals
  • Conclusion
  • Chapter 1 Notebook
• 2. A Deep Dive into AI for Project Managers
  • Key AI Topics
    • Evolution of AI Technologies
      • Prelude to AI (up to 1950)
      • The early days (the 50s)
      • The rise of AI (up to 1974)
      • The first AI winter (1974 to 1980)
      • Rebound and second AI winter (1980s)
      • The ML era (1990s and early 2000s)
      • The deep learning comeback (2006 to the 2010s)
      • The generative AI explosion (2020 onward)
    • Main AI Capabilities
    • Types of AI Learning
    • Additional AI Concepts
  • Building Blocks for AI Systems
    • Machine Learning
      • Machine learning concepts
      • Machine learning models
    • Deep Learning (DL)
    • Natural Language Processing and Generative AI
  • Conclusion
  • Chapter 2 Notebook
• 3. The Role of the AI Project Manager
  • Project Management and Hybrid Skills
    • AI and Data Science Knowledge
    • Proficiency in Agile Methodologies as Adapted to AI
    • Communication and Collaboration Skills
    • Data Management Skills
    • Data-Driven Decision-Making Capabilities
    • Leadership and Change Management Skills
  • Key Responsibilities of the AI Project Manager
    • The Core Value Proposition of an AI PM
      • Managing AI project stakeholders
        • Data scientists and AI engineers
        • MLOps and DevOps teams
        • Product owners and business stakeholders
        • Legal, compliance, and ethics advisors
        • Sponsors, clients/users, and executives
      • Building Alignment Across Stakeholders
    • The Responsibilities of an AI PM
      • Tier 1: Strategic alignment and ethical oversight
      • Tier 2: Cross-functional orchestration
      • Tier 3: Technical and data lifecycle stewardship
    • Managing AI Teams
      • Coordination with technical roles
        • Establishing common understandings
        • Establishing cadence and flow
        • Designing handoffs between roles
        • Scaling practice and culture
      • Evolution of roles due to GenAI
      • Emerging disciplines and the new shape of collaboration
      • AI team gaps analysis
        • Visualizing the gaps
        • Recognizing risk clusters
        • Turning gaps into growth
  • Bonus Track: ADRIAN Framework for AI PM Hiring
  • Conclusion
  • Chapter 3 Notebook
• 4. Applied Approach to AI Project Management
  • The EMED Methodology for End-to-End AI Project Management
    • Phase 1: Exploration
      • Strategic alignment of AI
        • Cost leadership
        • Differentiation
        • Focus approach
      • Use Case Prioritization
      • Impact of data on AI
      • Data analysis and profiling
        • The technical perspective
        • The business perspective
    • Phase 2: Mobilization
      • Required infrastructure
      • Tools and technologies
      • People and competencies
      • Data sources
      • Scientific approach
      • Ethical and legal aspects
    • Phase 3: Execution
      • Preparing your sprint zero
      • Analyzing and adapting to the project context
      • Supporting experimentation
      • Preparing users for adoption
      • Expanding the communication channels
    • Phase 4: Delivery
  • Useful Project Management Methodologies
    • Waterfall: A Foundational Approach from Traditional Project Management
    • Agile: An Adaptive PM Approach
      • Kanban: A visual flow approach to Agile work management
      • Scrum: An Agile framework for project management
        • Roles: The people
        • Scrum events
        • Artifacts: The tools
      • Lean: An approach to maximize value and reduce waste
      • Extreme Programming: An approach for software development
        • Engineering practices (technical excellence)
        • Management practices (delivery and review)
        • Design practices (managing complexity)
  • Recommended Hybrid Approach
  • Conclusion
  • Chapter 4 Notebook
• 5. PM Considerations During the Technical AI Lifecycle
  • The Notion of an AI Lifecycle
    • Technical AI Lifecycles
      • Cross-Industry Standard Process for Data Mining (CRISP-DM)
      • Microsofts Team Data Science Process
      • MLOps lifecycle
      • Evolution to GenAIOps/LLMOps
    • Other AI Lifecycles
      • NIST AI frameworks lifecycle
      • Lifecycles from ISO standards
      • Other relevant resources
  • AI Project Management Considerations by Stage
    • Stage 1: Ideation and Problem Definition
      • Use case discovery
      • Use case evaluation
      • Data source availability and mapping
      • Premortem analysis
      • Prioritization and long-term planning
    • Stage 2: Data Collection and Preparation
      • Exploratory data analysis
      • Data source evaluation
      • Data transformation and pipeline documentation
      • Data governance and compliance evaluation
    • Stage 3: AI Model Development and Experimentation
      • Modeling approach
      • Role of human knowledge
      • Baseline (naive) versus best model
      • Resource planning
      • Model information for compliance
      • Access to models
      • Explaining results
    • Stage 4: AI Evaluation and Validation
      • Performance metrics
      • Safety testing evaluation
      • Documented results
    • Stage 5: Deployment and AI System Integration
      • Infrastructure considerations for AI at scale
      • API development for AI model deployment
      • CI/CD and MLOps for AI model automation
      • Model retraining pipelines and optimization strategies
      • Scaling AI solutions for enterprise use
      • General cybersecurity
    • Stage 6: Maintenance and End of AI Lifecycle
      • AI system and model versioning
      • AI incidents management
      • Real-time monitoring and AI model auditing
      • AI model drift detection and retraining strategies
      • Documented end of lifecycle
  • Lifecycle for Generative AI Model Training
    • Pretraining
      • Self-supervised learning (SSL)
      • Vectorization
      • Expanding multimodal
      • Data augmentation
      • Distributed training
      • Mixture of experts
    • Posttraining
      • Fine-tuning
      • Reinforcement learning from human feedback
      • Pruning
      • Distillation
      • Quantization
      • AI red teaming (AI safety)
    • Inference
      • Customization
      • Optimization
  • Conclusion
  • Chapter 5 Notebook
• 6. Tools for Managing AI Projects
  • The AI Project Management Tool Trifecta
    • Project Management and Board Tools for Progress Tracking
    • Documentation and Tracking Tools for AI Knowledge Management
    • Code Repository Tools for Hosting and Deployment
  • Your AI-Enabled Tools for PM Productivity
    • Augmented Task Management
    • Discussions and Project Knowledge Collection
    • Accelerated Ideation and Prototyping
  • The Technology Stack for AI Projects
    • AI Infrastructure
      • CPUs and GPUs
      • Other computing units
    • Platforms
      • AI cloud studios
      • Development and deployments
      • Underlying data layer
    • Development Suites
      • Code assistants and prototyping tools with generative AI
      • Libraries for non-LLM models
      • New toolkits for generative AI
    • Governance and Compliance
  • Tool and Vendor Templates
    • General Tooling Checklist
    • Tool Analysis and Evaluation
      • Vendor profile
      • Tool scope
      • Usage considerations
  • Conclusion
  • Chapter 6 Notebook
• 7. Tales of AI Project Innovation
  • Learnings and Mistakes
  • Leveraging the Power of Mission
  • Managing the Human Aspect
  • Dealing with AI Impostors
  • Spotting the Red Flags
  • Respecting the Profession
  • Fear of Roadmapping
  • In Closing
  • Chapter 7 Notebook
• Index

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