book

The AI Product Manager's Handbook

Name: The AI Product Manager's Handbook
Author: Irene Bratsis
ISBN: 9781804612934

by Irene Bratsis

February 2023

Beginner

250 pages

8h 19m

English

Packt Publishing

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The AI Product Manager’s Handbook
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversConventions usedGet in touchShare Your ThoughtsDownload 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 AIML versus DL – understanding the differenceMLDLLearning types in MLSupervised learningUnsupervised learningSemi-supervised learningReinforcement learningThe order – what is the optimal flow and where does every part of the process live?Step 1 – Data availability and centralizationStep 2 – Continuous maintenanceDatabaseData warehouseData lake (and lakehouse)Data pipelinesManaging projects – IaaSDeployment strategies – what do we do with these outputs?Shadow deployment strategyA/B testing model deployment strategyCanary deployment strategySucceeding in AI – how well-managed AI companies do infrastructure rightThe promise of AI – where is AI taking us?SummaryAdditional resourcesReferences
Chapter 2: Model Development and Maintenance for AI Products
Understanding the stages of NPDStep 1 – DiscoveryStep 2 – DefineStep 3 – DesignStep 4 – ImplementationStep 5 – MarketingStep 6 – TrainingStep 7 – LaunchModel types – from linear regression to neural networksTraining – when is a model ready for market?Deployment – what happens after the workstation?Testing and troubleshootingRefreshing – the ethics of how often we update our modelsSummaryAdditional resourcesReferences
Chapter 3: Machine Learning and Deep Learning Deep Dive
The old – exploring MLThe new – exploring DLInvisible influencesA brief history of DLTypes of neural networksEmerging technologies – ancillary and related techExplainability – optimizing for ethics, caveats, and responsibilityAccuracy – optimizing for successSummaryReferences
Chapter 4: Commercializing AI Products
The professionals – examples of B2B products done rightThe artists – examples of B2C products done rightThe pioneers – examples of blue ocean productsThe rebels – examples of red ocean productsThe GOAT – examples of differentiated disruptive and dominant strategy productsThe dominant strategyThe disruptive strategyThe differentiated strategySummaryReferences
Chapter 5: AI Transformation and Its Impact on Product Management
Money and value – how AI could revolutionize our economic systemsGoods and services – growth in commercial MVPsGovernment and autonomy – how AI will shape our borders and freedomSickness and health – the benefits of AI and nanotech across healthcareBasic needs – AI for GoodSummaryAdditional resourcesReferences
Part 2 – Building an AI-Native Product
Chapter 6: Understanding the AI-Native Product
Stages of AI product developmentPhase 1 – IdeationPhase 2 – Data managementPhase 3 – Research and developmentPhase 4 – DeploymentAI/ML product dream teamAI PMAI/ML/data strategistsData engineerData analystData scientistML engineerFrontend/backend/full stack engineersUX designers/researchersCustomer successMarketing/sales/go-to-market teamInvesting in your tech stackProductizing AI-powered outputs – how AI product management is differentAI customizationSelling AI – product management as a higher octave  of salesSummaryReferences

Chapter 7: Productizing the ML Service
Understanding the differences between AI and traditional software productsHow are they similar?How are they different?B2B versus B2C – productizing business modelsDomain knowledge – understanding the needs of your marketExperimentation – discover the needs of your collectiveConsistency and AIOps/MLOps – reliance and trustPerformance evaluation – testing, retraining, and hyperparameter tuningFeedback loop – relationship buildingSummaryReferences
Chapter 8: Customization for Verticals, Customers, and Peer Groups
Domains – orienting AI toward specific areasUnderstanding your marketUnderstanding how your product design will serve your marketBuilding your AI product strategyVerticals – examination into four areas (FinTech, healthcare, consumer goods, and cybersecurity)FinTechHealthcareCybersecurityAnomaly detection and user and entity behavior analyticsValue metrics – evaluating performance across verticals and peer groupsObjectives and key resultsKey performance indicatorsThought leadership – learning from peer groupsSummaryReferences
Chapter 9: Macro and Micro AI for Your Product
Macro AI – Foundations and umbrellasMLRoboticsExpert systemsFuzzy logic/fuzzy matchingMicro AI – Feature levelML (traditional/DL/computer vision/NLP)RoboticsExpert systemsFuzzy logic/fuzzy matchingSuccesses – Examples that inspireLensaPeriGenChallenges – Common pitfallsEthicsPerformanceSafetySummaryReferences
Chapter 10: Benchmarking Performance, Growth Hacking, and Cost
Value metrics – a guide to north star metrics, KPIs and OKRsNorth star metricsKPIs and other metricsOKRs and product strategyHacking – product-led growthThe tech stack – early signalsCustomer Data Platforms (CDPs)Customer Engagement Platforms (CEPs)Product analytics toolsA/B testing toolsData warehousesBusiness Intelligence (BI) toolsGrowth-hacking toolsManaging costs and pricing – AI is expensiveSummaryReferences
Part 3 – Integrating AI into Existing Non-AI Products
Chapter 11: The Rising Tide of AI
Evolve or die – when change is the only constantThe fourth industrial revolution – hospitals used to  use candlesWorking with a consultantWorking with a third partyThe first hireThe first AI teamNo-code toolsFear is not the answer – there is more to gain than lose  (or spend)Anticipating potential risksSummary
Chapter 12: Trends and Insights across Industry
Highest growth areas – Forrester, Gartner, and McKinsey researchEmbedded AI – applied and integrated use casesEthical AI – responsibility and privacyCreative AI – generative and immersive applicationsAutonomous AI development – TuringBotsTrends in AI adoption – let the data speak for itselfGeneral trendsEmbedded AI – applied and integrated use casesEthical AI – responsibility and privacyCreative AI – generative and immersive applicationsAutonomous AI development – TuringBotsLow-hanging fruit – quickest wins for AI enablementSummaryReferences
Chapter 13: Evolving Products into AI Products
Venn diagram – what’s possible and what’s probableList 1 – valueList 2 – scopeList 3 – reachData is king – the bloodstream of the companyPreparation and researchQuality partnershipBenchmarkingThe data teamDefining successCompetition – love your enemiesProduct strategy – building a blueprint that works for everyoneProduct strategyRed flags and green flags – what to look for and watch out forRed flagsGreen flagsSummaryAdditional resources
Index
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Overview

Discover how to lead cutting-edge AI innovations with 'The AI Product Manager's Handbook.' This book equips you with the insights and tools needed to excel in developing, launching, and managing AI-integrated products.

What this Book will help me do

Learn to build AI-native products using minimal resources effectively.
Understand the integration of machine learning into product development processes.
Master collaborating across teams for deploying efficient AI solutions.
Evaluate the cost-effectiveness and challenges in using AI technologies.
Gain novel perspectives on the ethical and societal implications of AI.

Author(s)

Irene Bratsis is an experienced product expert with deep insights into AI application in product management. She bridges technical complexity with practical strategies in an approachable writing style, making complex topics accessible to professionals aiming to stay at the forefront of AI advancements.

Who is it for?

This book is ideal for product managers and professionals with foundational knowledge of AI wanting to enhance their ability to use AI to innovate. It serves as a roadmap for leveraging AI in product strategies and management.

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