Trusted by forward-thinking ML teams
ML Testing systems are broken
Enter Giskard: Fast ML Testing at scale
Who is it for?
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.
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
name="My QA bot",
description="An AI assistant that...",
Enable collaborative AI Quality Assurance at scale
Monitor your LLM-based applications
“Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!”
"Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!"
"Giskard has become a strong partner in our purpose for ethical AI. It delivers the right tools for releasing fair and trustworthy models."
"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."
Join the community
Thought leadership articles about ML Quality: Risk Management, Robustness, Efficiency, Reliability & Ethics
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.
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.
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.