Trusted by future-driven AI teams
AI pipelines are broken
Enter Giskard: Fast AI Testing at scale
Open-source & easy to integrate
In a few lines of code, identify vulnerabilities that may affect the performance, fairness & security of your model.
Directly in your Python notebook or Integrated Development Environment (IDE).
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
name="My QA bot",
description="An AI assistant that...",
Enable collaborative AI Quality Assurance
Monitor your LLM-based applications
Who is it for?
“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."
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Thought leadership articles about AI Quality: Performance, Robustness, Ethics, Risk & Governance
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Data Drift Monitoring with Giskard
Learn how to effectively monitor and manage data drift in machine learning models to maintain accuracy and reliability. This article provides a concise overview of the types of data drift, detection techniques, and strategies for maintaining model performance amidst changing data. It provides data scientists with practical insights into setting up, monitoring, and adjusting models to address data drift, emphasising the importance of ongoing model evaluation and adaptation.
EU AI ACT: 8 Takeaways from the Council's Final Approval
The Council of the EU has recently voted unanimously on the final version of the European AI Act. It’s a significant step forward in its efforts to legislate the first AI law in the world. The Act establishes a regulatory framework for the safe use and development of AI, categorizing AI systems according to their associated risk. In the coming months, the text will enter the last stage of the legislative process, where the European Parliament will have a final vote on the AI Act.