ilastik is an interactive machine learning tool designed to facilitate (bio)image analysis for users without substantial computational expertise. It offers pre-defined workflows for tasks such as image segmentation, object classification, counting, and tracking. Users can adapt these workflows by providing sparse training annotations, which are used to train nonlinear classifiers. ilastik can handle data in up to five dimensions and runs operations on-demand, allowing for interactive prediction on large datasets. The tool includes detailed workflows and case studies, demonstrating its effectiveness in various biological experiments. ilastik is free, open-source software available for Linux, MacOS, and Windows, and it provides a user-friendly interface and optimized implementation for fast feedback during training. The article also discusses the limitations and performance expectations of the tool, emphasizing the importance of validating the trained algorithm in different parts of the data. Additionally, it highlights the potential for integrating ilastik with other image analysis tools and machine-learning-based software.ilastik is an interactive machine learning tool designed to facilitate (bio)image analysis for users without substantial computational expertise. It offers pre-defined workflows for tasks such as image segmentation, object classification, counting, and tracking. Users can adapt these workflows by providing sparse training annotations, which are used to train nonlinear classifiers. ilastik can handle data in up to five dimensions and runs operations on-demand, allowing for interactive prediction on large datasets. The tool includes detailed workflows and case studies, demonstrating its effectiveness in various biological experiments. ilastik is free, open-source software available for Linux, MacOS, and Windows, and it provides a user-friendly interface and optimized implementation for fast feedback during training. The article also discusses the limitations and performance expectations of the tool, emphasizing the importance of validating the trained algorithm in different parts of the data. Additionally, it highlights the potential for integrating ilastik with other image analysis tools and machine-learning-based software.