Giskard - ML Testing & Quality Framework

Summary

Giskard is an open-source Python framework that brings systematic testing to machine learning models, treating AI quality assurance with the same rigor as traditional software testing. Unlike general ML monitoring tools, Giskard specifically focuses on proactive vulnerability detection, offering automated scans for bias, performance degradation, data leakage, and robustness issues across both traditional ML models and large language models (LLMs). Born from the recognition that most ML failures happen silently in production, Giskard provides a comprehensive testing suite that catches problems before they impact users.

What makes this different

Traditional ML evaluation typically stops at accuracy metrics and basic performance benchmarks. Giskard extends far beyond this by implementing domain-specific vulnerability scans that mirror real-world failure modes. The framework automatically generates adversarial test cases, detects spurious correlations, and identifies potential fairness issues without requiring extensive manual test creation.

What sets Giskard apart is its dual focus on automated scanning and human-interpretable results. The tool doesn't just flag potential issues—it provides detailed explanations of why a model might be vulnerable, complete with suggested remediation steps. For LLMs specifically, it includes specialized tests for prompt injection vulnerabilities, hallucination detection, and output consistency across similar inputs.

Core testing capabilities

• Automated Vulnerability Detection: Scans models for common ML pitfalls including data leakage, overfitting indicators, and distribution shifts. The system runs predefined test suites based on your model type and domain.
• Bias and Fairness Testing: Implements multiple fairness metrics and automatically tests for discriminatory behavior across protected attributes. Goes beyond simple demographic parity to include equalized odds and calibration testing.
• LLM-Specific Evaluations: Specialized test suite for language models covering factual accuracy, safety filtering effectiveness, and consistency in responses to semantically similar prompts.
• Custom Test Creation: Python-based DSL for writing domain-specific tests, allowing teams to encode business rules and regulatory requirements directly into their testing pipeline.
• Performance Regression Detection: Continuous monitoring capabilities that flag when model performance degrades on key segments or use cases.

Getting hands-on

Installation is straightforward via pip, and Giskard integrates with popular ML frameworks including scikit-learn, PyTorch, TensorFlow, and Hugging Face transformers. The basic workflow involves wrapping your trained model and dataset, then running either automated scans or custom test suites.

For LLM testing, you can connect directly to API-based models or local deployments. The framework handles the complexity of generating appropriate test cases and interpreting results across different model architectures.

Giskard generates detailed HTML reports with interactive visualizations, making it easy to share findings with both technical and non-technical stakeholders. The reports include severity rankings and actionable recommendations for addressing identified issues.

Who this resource is for

• ML Engineers and Data Scientists building production models who need systematic quality assurance beyond basic accuracy metrics. Particularly valuable for teams working in regulated industries where model failures carry significant consequences.
• AI Safety Teams responsible for ensuring responsible AI deployment. Giskard's bias detection and vulnerability scanning capabilities provide concrete evidence for safety assessments.
• MLOps Engineers implementing continuous integration for ML pipelines. The framework integrates well with existing CI/CD systems and provides automated quality gates for model deployment.
• Compliance Teams needing documentation of model testing for regulatory purposes. Giskard's detailed reporting helps satisfy audit requirements in finance, healthcare, and other regulated sectors.
• Organizations deploying LLMs either through APIs or self-hosted solutions. The specialized LLM testing capabilities address unique risks like prompt injection and harmful content generation that traditional ML testing doesn't cover.