Machine learning (ML) model testing is essential for ensuring that models perform effectively in production settings, addressing challenges like concept drift and data integrity issues that may not appear during initial training phases. The article explores various tools available for ML model testing, some of which are open-source, like DeepChecks and Drifter-ML, while others require subscriptions, such as Kolena.io and Robust Intelligence. These tools offer functionalities like detecting data drift, finding anomalies, checking model integrity, and providing automated testing suites. DeepChecks, for instance, provides a structured framework with checks, conditions, and suites to perform detailed inspections, while Kolena focuses on creating more focused datasets for granular testing. Subscription-based tools like Robust Intelligence offer end-to-end solutions with features like AI stress testing and continuous monitoring. Hybrid frameworks like Etiq.ai provide both offline and online implementations with various tiers of usage to suit different user needs. The choice of tool depends on specific project requirements, scale, and budget, with options available for both small-scale models and large enterprise solutions.