1. Use case: Gradio is primarily focused on enabling easy and quick web-based UI creation for machine learning models, making it an ideal tool for quick prototyping and deployment. On the other hand, sktime is specifically designed for time series data analysis tasks, offering a wide range of algorithms and functionalities tailored for time series forecasting, classification, and clustering.

2. Model Support: Gradio supports a variety of machine learning models, including deep learning models, for image, text, and tabular data. In contrast, sktime is specialized in time series analysis, providing algorithms specifically tailored for time series forecasting and classification tasks, such as ARIMA, KNN, and Random Forest for time series data.

3. Deployment: Gradio allows easy deployment of machine learning models with the generated UI on multiple platforms, including web browsers and cloud services, making it convenient for sharing and showcasing models. On the other hand, sktime is more focused on the development and evaluation of time series algorithms within the Python environment, with less emphasis on seamless deployment options.

4. Community and Support: Gradio has a growing community of data scientists and developers, enabling collaborative model building and sharing of UI templates. In comparison, sktime has a dedicated community focusing on time series analysis, providing specialized support and resources for time series modeling challenges and solutions.

5. Learning Curve: Gradio is designed to have a user-friendly interface, with drag-and-drop features for building UIs, making it accessible for users with minimal coding experience. In contrast, sktime requires a certain level of familiarity with time series analysis concepts and Python programming, as it offers a more specialized and in-depth toolkit for time series modeling.

6. Customization Options: Gradio provides a range of options for customizing the UI appearance, input fields, and output formats to suit different user preferences and application requirements. On the other hand, sktime focuses more on providing robust algorithms and methods for time series analysis, with less emphasis on UI customization options.

In Summary, Gradio and sktime differ in focus, model support, deployment options, community support, learning curve, and customization features, catering to distinct user needs in machine learning and time series analysis domains.