Machine learning (ML) models are powerful tools for solving complex problems in various industries, but many organizations still view their ML projects and data science teams as siloed domains within their business. This article explores the concept of the ML product, which involves transforming a model into a product that drives value for users. Key points include understanding user needs, setting clear goals, visualizing the end result, aligning stakeholders, asking questions, measuring success, and communicating progress effectively. By adopting a more integrated, product-driven approach to ML models, businesses can create valuable products that drive real-world results.