Getting familiar with the active ML tools

Throughout this book, we’ve introduced and discussed several key active ML tools and labeling platforms, including Lightly, Encord, LabelBox, Snorkel AI, Prodigy, modAL, and Roboflow. To further enhance your understanding and assist you in selecting the most suitable tool for your specific project needs, let’s revisit these tools with expanded insights and introduce a few additional ones:

• modAL (https://modal-python.readthedocs.io/en/latest/): This is a flexible and modular active ML framework in Python, designed to seamlessly integrate with scikit-learn. It stands out for its extensive range of query strategies, which can be tailored to various active ML scenarios. Whether you are dealing with classification, regression, or clustering tasks, modAL provides a robust and intuitive interface for implementing active learning workflows.
• Label Studio (https://docs.humansignal.com/guide/active_learning.
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