Por Jose R. Zapata
Ultima actualización: 16/Feb/2025
In the field of data science, effectively communicating insights and results is as crucial as the analysis itself. A well-crafted demo allows stakeholders, including non-technical users, to interact with machine learning models and data-driven insights in an intuitive manner.
There are different Python-based frameworks for this purpose like: Streamlit, Taipy, Gradio, among others. These tools provide a simple way to create interactive web applications that showcase the core functionalities of a data science project.
Creating a demo is a step in bridging the gap between machine learning models and real-world usability. It fosters engagement, improves interpretability, and ensures that stakeholders can derive meaningful insights from data science projects. By leveraging interface tools, data scientists can transform complex analyses into user-friendly applications that drive impact and decision-making.