Learn how to build a Streamlit data validation app using Deepchecks test suites and pandas-profiling reports in this hands-on tutorial. Explore the process of integrating Deepchecks, an open-source Python library for data scientists and ML engineers, into a Streamlit application. Discover how to render test suites, implement file uploading, create profile and validation choice menus, and integrate pandas-profiling. Follow along as the instructor demonstrates Deepchecks test integration, test validation, and JSON filtering of test results. By the end of this 52-minute tutorial, gain the skills to create a comprehensive Python application for displaying DeepChecks reports and enhancing your data validation workflows.