The AI product manager's handbook develop a product that takes advantage of machine learning to solve AI problems
Unlock the power of AI and become a successful product manager with this comprehensive guide covering the strategies, techniques, and tools to build, launch, and manage AI products. From the basics of AI to navigating ethical and legal considerations, this book covers everything you need to know to...
| Other Authors: | |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Birmingham ; Mumbai :
Packt Publishing
[2023]
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| Edition: | 1st ed |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009726539706719 |
Table of Contents:
- 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
- Chapter 6: Understanding the AI-Native Product
- Stages of AI product development
- Phase 1 - Ideation
- Phase 2 - Data management
- Phase 3 - Research and development
- Phase 4 - Deployment
- AI/ML product dream team
- AI PM
- AI/ML/data strategists
- Data engineer
- Data analyst
- Data scientist
- ML engineer
- Frontend/backend/full stack engineers
- UX designers/researchers
- Customer success
- Marketing/sales/go-to-market team
- Investing in your tech stack
- Productizing AI-powered outputs - how AI product management is different
- AI customization
- Selling AI - product management as a higher octave of sales
- Summary
- References
- Chapter 7: Productizing the ML Service
- Understanding the differences between AI and traditional software products.
- How are they similar?
- How are they different?
- B2B versus B2C - productizing business models
- Domain knowledge - understanding the needs of your market
- Experimentation - discover the needs of your collective
- Consistency and AIOps/MLOps - reliance and trust
- Performance evaluation - testing, retraining, and hyperparameter tuning
- Feedback loop - relationship building
- Summary
- References
- Chapter 8: Customization for Verticals, Customers, and Peer Groups
- Domains - orienting AI toward specific areas
- Understanding your market
- Understanding how your product design will serve your market
- Building your AI product strategy
- Verticals - examination into four areas (FinTech, healthcare, consumer goods, and cybersecurity)
- FinTech
- Healthcare
- Cybersecurity
- Anomaly detection and user and entity behavior analytics
- Value metrics - evaluating performance across verticals and peer groups
- Objectives and key results
- Key performance indicators
- Thought leadership - learning from peer groups
- Summary
- References
- Chapter 9: Macro and Micro AI for Your Product
- Macro AI - Foundations and umbrellas
- ML
- Robotics
- Expert systems
- Fuzzy logic/fuzzy matching
- Micro AI - Feature level
- ML (traditional/DL/computer vision/NLP)
- Robotics
- Expert systems
- Fuzzy logic/fuzzy matching
- Successes - Examples that inspire
- Lensa
- PeriGen
- Challenges - Common pitfalls
- Ethics
- Performance
- Safety
- Summary
- References
- Chapter 10: Benchmarking Performance, Growth Hacking, and Cost
- Value metrics - a guide to north star metrics, KPIs and OKRs
- North star metrics
- KPIs and other metrics
- OKRs and product strategy
- Hacking - product-led growth
- The tech stack - early signals
- Customer Data Platforms (CDPs)
- Customer Engagement Platforms (CEPs)
- Product analytics tools.
- A/B testing tools
- Data warehouses
- Business Intelligence (BI) tools
- Growth-hacking tools
- Managing costs and pricing - AI is expensive
- Summary
- References
- Part 3 - Integrating AI into Existing Non-AI Products
- Chapter 11: The Rising Tide of AI
- Evolve or die - when change is the only constant
- The fourth industrial revolution - hospitals used to use candles
- Working with a consultant
- Working with a third party
- The first hire
- The first AI team
- No-code tools
- Fear is not the answer - there is more to gain than lose (or spend)
- Anticipating potential risks
- Summary
- Chapter 12: Trends and Insights across Industry
- Highest growth areas - Forrester, Gartner, and McKinsey research
- Embedded AI - applied and integrated use cases
- Ethical AI - responsibility and privacy
- Creative AI - generative and immersive applications
- Autonomous AI development - TuringBots
- Trends in AI adoption - let the data speak for itself
- General trends
- Embedded AI - applied and integrated use cases
- Ethical AI - responsibility and privacy
- Creative AI - generative and immersive applications
- Autonomous AI development - TuringBots
- Low-hanging fruit - quickest wins for AI enablement
- Summary
- References
- Chapter 13: Evolving Products into AI Products
- Venn diagram - what's possible and what's probable
- List 1 - value
- List 2 - scope
- List 3 - reach
- Data is king - the bloodstream of the company
- Preparation and research
- Quality partnership
- Benchmarking
- The data team
- Defining success
- Competition - love your enemies
- Product strategy - building a blueprint that works for everyone
- Product strategy
- Red flags and green flags - what to look for and watch out for
- Red flags
- Green flags
- Summary
- Additional resources
- Index
- Other Books You May Enjoy.
