What is Giskard?
Fast LLM & ML testing at scale 🛡️ Detect hallucinations & biases automatically 🔍 Enterprise Testing Hub ☁️ Self-hosted / cloud 🤝 Integrated with 🤗, MLFlow, W&B From tabular models to LLMs, Giskard handles everything! https://github.com/Giskard-AI/giskard
Problem
Developing and deploying Large Language Models (LLMs) and Machine Learning (ML) models come with challenges such as detecting hallucinations, biases, and ensuring comprehensive testing at scale. The existing solutions often lack the capability to automatically detect these issues, making the process cumbersome and less efficient.
Solution
Giskard is an open-source testing framework for LLMs & ML models that offers fast testing at scale, automatic detection of hallucinations & biases, and an Enterprise Testing Hub for centralized testing management. It allows for both self-hosted and cloud deployments and integrates with popular tools such as 🤗, MLFlow, and W&B, covering everything from tabular models to LLMs.
Customers
The primary users of Giskard are data scientists, ML engineers, and enterprises that develop and deploy large language models and machine learning solutions, looking for efficient, scalable testing solutions.
Unique Features
Giskard distinguishes itself by offering an open-source solution that integrates automatic detection of hallucinations and biases, supports both self-hosted and cloud deployments, and provides comprehensive testing across a variety of model types. Its integration with popular ML tools and platforms further enhances its utility in the machine learning community.
User Comments
Comprehensive and powerful tool for ML model testing
Open-source aspect greatly appreciated by the community
Enhances the reliability of machine learning deployments
Useful for detecting biases and hallucinations in models
Flexible deployment options considered a major advantage
Traction
As Giskard is an open-source product, specific metrics such as MRR/ARR, number of users, or financing details aren't directly applicable. However, the project's visibility on platforms like GitHub and ProductHunt, along with its integration capabilities with widely used ML tools, suggest a growing interest and adoption within the developer and data science communities.
Market Size
The global machine learning market size is projected to grow from $15.5 billion in 2021 to $152.24 billion by 2028, at a CAGR of 38.6%. Given Giskard's positioning as a testing framework for LLMs & ML models, it is positioned within this expansive growth, catering to the increasing demand for reliable and efficient ML model testing solutions.
