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Transforming Machine Learning with Gradio
This chapter explores Gradio's significant influence on machine learning workflows, especially within interdisciplinary teams, by enhancing model trust and robustness through interactive demos. It highlights real-world examples, including the prediction of pacemaker presence in echocardiograms, while addressing concerns around model reliability and biases. Additionally, the chapter discusses the platform's evolution with Hugging Face integration and its focus on exploratory research, emphasizing the community's innovative uses and the importance of interactivity in data science.
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