Description

what is Deepchecks?

Deepchecks is a holistic open-source solution for all your AI & ML validation needs, enabling you to thoroughly test your data and models from research to production. It provides comprehensive support for your testing requirements, from examining data integrity and assessing distributions, to validating data splits, comparing models, and evaluating their performance across the model’s entire development process.

Key Features

• Data validation: Checks your data for quality issues like missing values, outliers, and inconsistencies.
• Model validation: Validates your model’s behavior and performance on different datasets.
• Drift monitoring: Monitors your model’s performance over time to detect any drift.
• Explainability: Helps you understand why your model makes certain predictions.
• Fairness: Checks for potential biases in your model’s predictions.
• Interpretability: Makes your model’s predictions more understandable.
• Reproducibility: Ensures that your model’s results can be reproduced.

Benefits :

• Improved data quality: Ensures that your models are trained on high-quality data.
• Improved model performance: Helps you build better models that generalize well to new data.
• Reduced risk of errors: Helps you identify and fix potential problems before they cause issues in production.
• Increased trust and transparency: Helps you build trust in your models with stakeholders.
• Efficiency: Automates many testing tasks, saving you time and effort.

Target audience:

• Machine learning engineers: Use Deepchecks to validate and test their models.
• Data scientists: Use Deepchecks to ensure the quality of their data.
• Software engineers: Use Deepchecks to integrate testing into their CI/CD pipelines.
• Researchers: Use Deepchecks to assess the robustness and fairness of their models.

Overall, Deepchecks is a valuable tool for anyone who wants to build and deploy reliable and trustworthy AI models.