Hello Product Hunt,
I'm Alex, here with Jean-Marie, Andrey, and the rest of the Giskard team. We're thrilled and slightly nervous to present Giskard 2.0. This has been 2 years in the making, involving a group of passionate ML engineers, ethicists, and researchers, in partnerships with leading standard organizations, such as AFNOR and ISO.
So, why Giskard? Because we understand the dilemma you face. Manually creating test cases, crafting reports, building dashboards, and enduring endless review meetings - testing ML models can take weeks, even months!
With the new wave of Large Language Models (LLMs), testing models becomes an even more impossible mission. The questions keep coming: Where to start? What issues to focus on? How to implement the tests? π«
Meanwhile, the pressure to deploy quickly is constant, often pushing models into production with unseen vulnerabilities. The bottleneck? ML Testing systems.
Our experience includes leading ML Engineering at Dataiku and years of research in AI Ethics. We saw many ML teams struggling with the same issues: slowed down by inefficient testing, allowing critical errors and biases to slip into production.
Current MLOps tools fall short. They lack transparency and donβt cover the full range of AI risks: robustness, fairness, security, efficiency, you name it. Add to this compliance to AI regulations, some of which could be punitive, costing up to 6% of your revenue (EU AI Act).
Enter Giskard:
π¦ A comprehensive ML Testing framework for Data Scientists, ML Engineers, and Quality specialists. It offers automated vulnerability detection, customizable tests, CI/CD integration, and collaborative dashboards.
π An open-source Python library for automatically detecting hidden vulnerabilities in ML and LLMs, tackling issues from robustness to ethical biases.
π An enterprise-ready Testing Hub application with dashboards and visual debugging, built to enable collaborative AI Quality Assurance and compliance at scale.
β Compatibility with the Python ML ecosystem, including Hugging Face, MLFlow, Weights & Biases, PyTorch, Tensorflow, and Langchain.
βοΈ A model-agnostic approach that serves tabular models, NLP, and LLMs. Soon, we'll also support Computer Vision, Recommender Systems, and Time Series.
Equip yourself with Giskard to defeat your AI Quality issues! π’π‘οΈ
We build in the open, so weβre welcoming your feedback, feature requests and questions.
For further information:
Website: https://www.giskard.ai/
GitHub: https://github.com/Giskard-AI/gi...
Discord Community: https://gisk.ar/discord
Best,
Alex & the Giskard Team
