> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/ai-product/intro.md). # Introduction The point of the path is a product a person uses and a metric the business can feel. After this chapter the reader can connect vision, teams, and a worked problem such as churn, fraud, growth, or discovery to the method that came before. The chapter moves from product practice, to the teams that ship it, to worked problems where a model meets a business metric. ## Practice A product needs a direction before a backlog, so [Product Vision & Strategy](/ai-product/product-vision-and-strategy.md) comes first: vision, strategy, roadmap, and positioning. Ideas then pile up without a frame, and [Ideation](/ai-product/ideation.md) gives ways to generate and compare them. Because a product is used through an interface, [User Experience Design (UX)](/ai-product/user-experience-design-ux.md) collects UX references and growth-design case studies. Someone has to own the product and the program, and [Product / Program Managers](/ai-product/product-managers.md) points at guides for AI product managers and data program managers. Product decisions need measurement, so [Product Tools](/ai-product/product-tools.md) compares customer-data platforms and product-analytics tools. Shared reading beats tribal memory, and [Product Management Resources](/ai-product/product-management-resources.md) gathers beginner lists, outcomes, north-star and market-fit notes, advanced reading, essays, and books. Data scientists still need go-to-market vocabulary, which [Business](/ai-product/business.md) introduces through a field guide. Model skill alone is not enough, and [Expanding Your Data Science Skills](/ai-product/expanding-your-data-science-skills.md) argues for learning the product, business, experience, marketing, full-stack, and Ops domains the work connects to. ## Teams Practice only ships through people. [Management](/ai-product/management.md) covers how people, metrics, and culture decide whether ML ships: management traits, OKRs and KPIs, teams, agile scaling, culture, and career development. [Data Science Management](/ai-product/data-science-management.md) narrows that to a data science team that still has to hire, interview, write, and stay legal. [Project & Program Management](/ai-product/project-and-program-management.md) gives big projects a delivery shape, and shows how PM, TPM, and tech-lead roles differ. ## Worked problems With practice and teams in place, the worked problems show a model meeting a business metric. [Marketing](/ai-product/marketing.md) is about metrics that matter, and [Growth](/ai-product/growth.md) is about product-led growth after a product ships. [Recommender Systems](/ai-product/recommender-systems.md) gives users a next item and shows how to evaluate that. A company wants to know who will leave, and [Churn Prediction](/ai-product/churn-prediction.md) argues that the treatment is the decision. A customer is worth more than one purchase, which is the subject of [Life Time Value (LTV)](/ai-product/life-time-value-ltv.md), and time-to-event as its own problem is [Survival Analysis](/ai-product/survival-analysis.md). Fraud is rare and costly, and [Fraud Detection](/ai-product/fraud-detection.md) collects handbooks, methods, and data sets for it. [Propaganda Detection](/ai-product/propaganda-detection.md) looks at fine-grained language, and [Drug Discovery](/ai-product/drug-discovery.md) at drugs that interact in combinations. Regression gets a concrete city problem in [NYC TAXI](/ai-product/examples.md), the taxi pickup problem. Finally, [Electronic Network Frequency Analysis](/ai-product/electronic-network-frequency-analysis.md) checks audio against the power grid. [Agents-Driven Organizations](https://cohenori.medium.com/agents-driven-organizations-4bc300fc5283) (January 2026) is the product-organization note for this chapter: Dr. Ori Cohen on moving from AI as a subordinate copilot to agents integrated as team members, and on what that enables, where it creates pressure, and the organizational dynamics it introduces. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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