The AI product manager's handbook : develop a product that takes advantage of machine learning to solve AI problems /

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Master the skills required to become an AI product manager and drive the successful development and deployment of AI products to deliver value to your organization. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Build products that leverage AI for the common good and co...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bratsis, Irene (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Artificial intelligence.
Product management.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Contributors
  • Table of Contents
  • Preface
  • Part 1
  • Lay of the Land
  • Terms, Infrastructure, Types of AI, and Products Done Well
  • Chapter 1: Understanding the Infrastructure and Tools for Building AI Products
  • Definitions
  • what is and is not AI
  • ML versus DL
  • understanding the difference
  • ML
  • DL
  • Learning types in ML
  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning
  • The order
  • what is the optimal flow and where does every part of the process live?
  • Step 1
  • Data availability and centralization
  • Step 2
  • Continuous maintenance
  • Database
  • Data warehouse
  • Data lake (and lakehouse)
  • Data pipelines
  • Managing projects
  • IaaS
  • Deployment strategies
  • what do we do with these outputs?
  • Shadow deployment strategy
  • A/B testing model deployment strategy
  • Canary deployment strategy
  • Succeeding in AI
  • how well-managed AI companies do infrastructure right
  • The promise of AI
  • where is AI taking us?
  • Summary
  • Additional resources
  • References
  • Chapter 2: Model Development and Maintenance for AI Products
  • Understanding the stages of NPD
  • Step 1
  • Discovery
  • Step 2
  • Define
  • Step 3
  • Design
  • Step 4
  • Implementation
  • Step 5
  • Marketing
  • Step 6
  • Training
  • Step 7
  • Launch
  • Model types
  • from linear regression to neural networks
  • Training
  • when is a model ready for market?
  • Deployment
  • what happens after the workstation?
  • Testing and troubleshooting
  • Refreshing
  • the ethics of how often we update our models
  • Summary
  • Additional resources
  • References
  • Chapter 3: Machine Learning and Deep Learning Deep Dive
  • The old
  • exploring ML
  • The new
  • exploring DL
  • Invisible influences
  • A brief history of DL
  • Types of neural networks
  • Emerging technologies
  • ancillary and related tech
  • Explainability
  • optimizing for ethics, caveats, and responsibility
  • Accuracy
  • optimizing for success
  • Summary
  • References
  • Chapter 4: Commercializing AI Products
  • The professionals
  • examples of B2B products done right
  • The artists
  • examples of B2C products done right
  • The pioneers
  • examples of blue ocean products
  • The rebels
  • examples of red ocean products
  • The GOAT
  • examples of differentiated disruptive and dominant strategy products
  • The dominant strategy
  • The disruptive strategy
  • The differentiated strategy
  • Summary
  • References
  • Chapter 5: AI Transformation and Its Impact on Product Management
  • Money and value
  • how AI could revolutionize our economic systems
  • Goods and services
  • growth in commercial MVPs
  • Government and autonomy
  • how AI will shape our borders and freedom
  • Sickness and health
  • the benefits of AI and nanotech across healthcare
  • Basic needs
  • AI for Good
  • Summary
  • Additional resources
  • References
  • Part 2
  • Building an AI-Native Product

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