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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
From tabular models to LLMs
Listed by Gartner
AI Trust, Risk and Security
# Get started
pip install giskard -U
pip install giskard -U
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Trusted by forward-thinking ML teams
Why?
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.
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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
Quality Management system for AI models
ML Testing library
An open-source Python library for automatically detecting hidden vulnerabilities in ML and LLMs, tackling issues from robustness to ethical biases.
AI Quality Hub
An enterprise-ready Testing Hub application with dashboards and visual debugging, built to enable collaborative AI Quality Assurance at scale.
LLMon (beta)
Monitor your LLMs and detect hallucinations, incorrect responses & toxicity. Choose SaaS or on-premise deployment
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 = giskard.demo.climate_qa_chain()
model = giskard.Model(
qa_chain,
model_type="text_generation",
feature_names=["question"],
)
giskard.scan(model)
qa_chain = giskard.demo.climate_qa_chain()
model = giskard.Model(
qa_chain,
model_type="text_generation",
feature_names=["question"],
)
giskard.scan(model)
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Enable collaborative AI Quality Assurance at scale
Entreprise-ready quality assurance platform to debug your ML models collaboratively.
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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.png)
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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.
Discord
All those interested in ML Quality are welcome here!
All resources
Thought leadership articles about ML Quality: Risk Management, Robustness, Efficiency, Reliability & Ethics
See all
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Ready. Set. Test!
Get started today
