Deepchecks
Deepchecks is a platform offering automated testing, validation, and real-time monitoring of machine learning models, ensuring high performance, reliability, and ethical compliance.
About Deepchecks
Deepchecks is an AI-powered platform designed to test, validate, and monitor machine learning (ML) models and large language models (LLMs).
It helps developers and businesses ensure that their AI models deliver high-quality, reliable, and compliant results.
With features like automated testing, real-time monitoring, and customizable evaluation metrics, Deepchecks allows users to identify performance issues, data drift, and ethical concerns such as fairness and bias.
It is ideal for applica...
It helps developers and businesses ensure that their AI models deliver high-quality, reliable, and compliant results.
With features like automated testing, real-time monitoring, and customizable evaluation metrics, Deepchecks allows users to identify performance issues, data drift, and ethical concerns such as fairness and bias.
It is ideal for applica...
Key Features
- Automated Testing: Easily test ML models and LLMs for performance and accuracy.
- Real-Time Monitoring: Detect data drift, concept drift, and performance degradation in real time.
- Customizable Evaluation Metrics: Tailor evaluation criteria to match business goals and use cases.
- Auto-Annotation Pipelines: Speed up the testing process with automated annotation and manual overrides for fine-tuning.
- Ethical AI Compliance: Ensure models are fair, safe, and free from harmful content.
- Root Cause Analysis: Identify issues and improve models with detailed insights.
Use Cases
- Model Validation: Validate LLMs in applications like summarization, text generation, and RAG.
- Performance Monitoring: Track ML model performance in real-time to catch issues early.
- AI Ethics Compliance: Ensure fairness and ethical standards are met in ML models.
- Edge Case Extraction: Automate the identification and analysis of edge cases for better model tuning.
- Cost Reduction: Minimize manual annotation and evaluation costs with automated processes.
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