Develop test suites for machine learning models and data using Deepchecks in this hands-on lab. Explore the Python library's extensive test suites, learn to compose checks with customizable conditions, and generate HTML reports for easy result interpretation. Master data validation tests, distribution analysis, and model validation techniques. Create custom tests tailored to specific needs, and integrate Deepchecks into ML pipelines for comprehensive model and data quality assurance. Apply these skills to Random Forest and Gradient Boosting Classifier models, gaining practical experience in enhancing the reliability and performance of machine learning projects.