The testing
framework for ML models

Eliminate risks of biases, performance issues & security holes in ML models. In <8 lines of code.

From tabular models to LLMs
Listed by Gartner
AI Trust, Risk and Security
# Get started
pip install giskard -U
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Trusted by forward-thinking ML teams


ML Testing systems are broken

ML teams spend weeks manually creating test cases, writing reports, and enduring endless review meetings.
MLOps tools don’t cover the full range of AI risks: robustness, fairness, efficiency, security, etc.
ML Testing practices are siloed and inconsistent across projects & teams.
Non compliance to new AI regulations can cost up to 6% of your revenue.

Enter Giskard: Fast ML Testing at scale

Stop wasting time on manual testing and writing evaluation reports.
Automatically detect errors, biases and security holes in your ML models.
Unify your ML Testing: use standardized methodologies for optimal model deployment.
Ensure compliance with AI regulations using our AI Quality management system

Who is it for?

Data scientists
ML Engineers
Quality specialists
You want to work with the best Open-source tools
You work on business-critical AI applications
You spend a lot of time evaluating & testing models
You prioritize quality, security, safety & performance in production
You care about Responsible AI principles: fairness, transparency, accountability

Open-source & easy to integrate

In a few lines of code, identify vulnerabilities that may affect the performance, fairness & reliability of your model. 

Directly in your notebook.

import giskard
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
name="My QA bot",
description="An AI assistant that...",
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Enable collaborative AI Quality Assurance at scale

Entreprise-ready quality assurance platform to debug your ML models collaboratively.
Try our latest beta release!

Monitor your LLM-based applications

Diagnose critical AI Safety risks in real-time, such as hallucinations, incorrect responses and toxicity in your LLM outputs. Works with any LLM API.
Try LLMon

“Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!”

Head of Data
Emeric Trossat

"Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!"

Head of Data
Emeric Trossat

"Giskard has become a strong partner in our purpose for ethical AI. It delivers the right tools for releasing fair and trustworthy models."

Head of Data Science
Arnault Gombert

"Giskard enables to integrate Altaroad business experts' knowledge into our ML models and test them."


"Giskard allows us to easily identify biases in our models and gives us actionable ways to deliver robust models to our customers."

Chief Science Officer
Maximilien Baudry

Join the community

Welcome to an inclusive community focused on ML Quality! Join us to share best practices, create new tests, and shape the future of AI safety standards together.


All those interested in ML Quality are welcome here!

All resources

Thought leadership articles about ML Quality: Risk Management, Robustness, Efficiency, Reliability & Ethics

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Giskard’s LLM Testing solution is launching on Product Hunt

Our LLM Testing solution is launching on Product Hunt 🚀

We have just launched Giskard v2, extending the testing capabilities of our library and Hub to Large Language Models. Support our launch on Product Hunt and explore our new integrations with Hugging Face, Weights & Biases, MLFlow, and Dagshub. A big thank you to our community for helping us reach over 1900 stars on GitHub.

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Mastering ML Model Evaluation with Giskard: From Validation to CI/CD Integration

Learn how to integrate vulnerability scanning, model validation, and CI/CD pipeline optimization to ensure reliability and security of your AI models. Discover best practices, workflow simplification, and techniques to monitor and maintain model integrity. From basic setup to more advanced uses, this article offers invaluable insights to enhance your model development and deployment process.

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How to address Machine Learning Bias in a pre-trained HuggingFace text classification model?

Machine learning models, despite their potential, often face issues like biases and performance inconsistencies. As these models find real-world applications, ensuring their robustness becomes paramount. This tutorial explores these challenges, using the Ecommerce Text Classification dataset as a case study. Through this, we highlight key measures and tools, such as Giskard, to boost model performance.

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Ready. Set. Test!
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