A machine learning toolbox is essential for teams to apply machine learning successfully in their products. The toolbox consists of three fundamental tools: a Feature Store, a Model Store, and an Evaluation Store. A Feature Store enables the centralization of feature transformations, allowing for offline and online serving, team collaboration, and version control. A Model Store serves as a central repository for models and model versions, enabling reproducibility, tracking lineage, and integration with other tools. An Evaluation Store provides performance metrics, monitoring, and evaluation capabilities to ensure model quality and continuous improvement. Additional tools like Data Annotation Platforms, Model Serving Platforms, and AI Orchestration Platforms can complement the toolbox by handling data annotation, model deployment, and workflow management, respectively. By leveraging these three core tools and additional complementary tools, teams can successfully apply machine learning in their products and achieve rapid innovation.