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Data Validation Model Evaluation Data Drift Detection Automated testing Performance Monitoring See all 
Data Validation Ensures datasets meet quality standards by detecting missing values, inconsistencies, anomalies, and schema mismatches early. Model Evaluation Evaluates model performance using metrics like accuracy, precision, recall, and custom KPIs across datasets and use cases. Data Drift Detection Continuously monitors incoming data to identify distribution shifts that may degrade model performance over time. Automated testing Runs predefined and customizable tests on data and models to catch issues before deployment or during updates. Performance Monitoring Tracks model performance in production, providing real-time insights into degradation and anomalies. Alerts & Notifications Sends alerts when anomalies, drift, or failures are detected, enabling quick response and mitigation actions. Version Comparison Compares different model or prompt versions to identify improvements and regressions before deployment decisions. API Integration Offers APIs for seamless integration with ML pipelines, data platforms, and CI/CD workflows for automation. Dashboard & Reporting Delivers interactive dashboards and reports for tracking model health, test results, and validation summaries. Annotations Supports human-in-the-loop workflows for labeling, reviewing, and improving datasets and model outputs. Security & Compliance Helps align AI systems with regulatory standards by monitoring data usage, privacy risks, and audit readiness. Scalability & Performance Handles large-scale datasets and models efficiently, ensuring consistent performance in enterprise environments. Concept Drift Detection Detects changes in relationships between inputs and outputs, ensuring models remain relevant in dynamic environments. | Brand Name | Deepchecks AI |
| Information | Evaluation for LLM-Based Apps. |
| Founded Year | 2019 |
| Director/Founders | Philip Tannor, Shir Chorev |
| Company Size | 1-100 Employees |
UAE
India
