The AI Product Manager’s Handbook: Develop a product that takes advantage of machine learning to solve AI problems
- Length: 250 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-02-28
- ISBN-10: 1804612936
- ISBN-13: 9781804612934
- Sales Rank: #3718301 (See Top 100 Books)
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 commercial success
- Take macro data and use it to show your customers you’re a source of truth
- Best practices and common pitfalls that impact companies while developing AI product
Book Description
Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed.
The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products.
You’ll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You’ll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you’ll stay ahead of the curve in the rapidly evolving field of AI and ML.
By the end of this book, you’ll have understood how to navigate the world of AI from a product perspective.
What you will learn
- Build AI products for the future using minimal resources
- Identify opportunities where AI can be leveraged to meet business needs
- Collaborate with cross-functional teams to develop and deploy AI products
- Analyze the benefits and costs of developing products using ML and DL
- Explore the role of ethics and responsibility in dealing with sensitive data
- Understand performance and efficacy across verticals
Who this book is for
This book is for product managers and other professionals interested in incorporating AI into their products. Foundational knowledge of AI is expected. If you understand the importance of AI as the rising fourth industrial revolution, this book will help you surf the tidal wave of digital transformation and change across industries.
The AI Product Manager’s Handbook Contributors About the author About the reviewer Preface Who this book is for What this book covers Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book 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 Why subscribe? 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