Ilastik is an interactive machine learning tool for (bio)image analysis that enables end users without computational expertise to perform image segmentation, object classification, counting, and tracking. It uses pre-defined workflows that can be adapted by users through interactive training annotations. The tool processes data in up to five dimensions (3D, time, and channels) and allows interactive prediction on data larger than RAM. Once trained, workflows can be applied to new data without further user interaction. Ilastik's backend is optimized for performance and can run on standard computers. It uses a Random Forest classifier by default, which is robust and easy to use for non-experts. The tool also supports various workflows, including pixel classification, object classification, counting, tracking, and carving. It can handle complex data types and is compatible with multiple image formats. Ilastik is open-source and available for Linux, macOS, and Windows. It provides a user-friendly interface and allows for interactive refinement of results. The tool is designed to be efficient and scalable, with a focus on usability and performance. It is particularly useful for biological image analysis, where manual annotation is time-consuming and error-prone. Ilastik can be integrated with other image analysis tools and supports collaboration through its open-source nature. The tool is continuously developed and improved, with a focus on making machine learning accessible to a wide range of users.Ilastik is an interactive machine learning tool for (bio)image analysis that enables end users without computational expertise to perform image segmentation, object classification, counting, and tracking. It uses pre-defined workflows that can be adapted by users through interactive training annotations. The tool processes data in up to five dimensions (3D, time, and channels) and allows interactive prediction on data larger than RAM. Once trained, workflows can be applied to new data without further user interaction. Ilastik's backend is optimized for performance and can run on standard computers. It uses a Random Forest classifier by default, which is robust and easy to use for non-experts. The tool also supports various workflows, including pixel classification, object classification, counting, tracking, and carving. It can handle complex data types and is compatible with multiple image formats. Ilastik is open-source and available for Linux, macOS, and Windows. It provides a user-friendly interface and allows for interactive refinement of results. The tool is designed to be efficient and scalable, with a focus on usability and performance. It is particularly useful for biological image analysis, where manual annotation is time-consuming and error-prone. Ilastik can be integrated with other image analysis tools and supports collaboration through its open-source nature. The tool is continuously developed and improved, with a focus on making machine learning accessible to a wide range of users.