Deepchecks

Deepchecks is a comprehensive evaluation solution for continuous validation of large language models (LLMs) and AI systems. It offers testing, CI/CD integration, and monitoring capabilities to reduce the risk during deployment and ensure the functioning of LLM-based applications. With Deepchecks, users can simplify compliance with AI-related policies, assess the performance of their LLM application, track and compare different combinations of prompts, models, and code.

Key Features:

1. Testing: Deepchecks allows users to run test suites on their data and models iteratively from within a notebook or an IDE. This helps in identifying issues early on in the development process.

2. CI/CD Integration: Users can integrate Deepchecks into their CI/CD pipeline using tools like GitHub Actions or Airflow. This ensures that re-trained models do not cause any issues when deployed to production.

3. Monitoring: Deepchecks provides monitoring capabilities to track data and models in production environments. This helps in ensuring that ML systems behave as expected over time.

Use Cases:

1. Research Phase Evaluation: Data scientists and ML engineers can use Deepchecks Open Source during the research phase to test their ML models on various datasets and iterate on improvements.

2. Production Deployment Confidence: By thoroughly assessing the performance of LLM applications using high-level metrics combined with examples, users can deploy their applications into production with confidence.

3. Compliance Simplification: Deepchecks simplifies compliance with AI-related policies, regulations, and soft laws by providing direct visibility into the functioning of LLM-based applications.

In conclusion, Deepchecks is a powerful tool for continuous evaluation of LLMs and AI systems throughout their lifecycle. Its testing, CI/CD integration, and monitoring features help reduce deployment risks while ensuring optimal performance in production environments.