The Giskard hub
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.
Monitoring LLMs, due to their broad knowledge and nuanced language handling, is complex. Ensuring accurate, relevant responses across scenarios, while mitigating risks like toxicity and misinformation, demands a robust monitoring solution. LLMon, tailored for LLMs, offers real-time analytics and control for their safe, ethical use in production. This article discusses leveraging LLMon to overcome LLM deployment challenges.
Explore how to use open-source Large Language Models (LLMs) to build AI customer service chatbots. We guide you through creating chatbots with LangChain and HuggingFace libraries, and how to evaluate their performance and safety using Giskard's testing framework.
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.
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.
Explore our tutorial on model fairness to detect hidden biases in machine learning models. Understand the flaws of traditional evaluation metrics with the help of the Giskard library. Our guide, packed with examples and a step-by-step process, shows you how to tackle data sampling bias and master feature engineering for fairness. Learn to create domain-specific tests and debug your ML models, ensuring they are fair and reliable.
SHAP stands for "SHapley Additive exPlanations", and is a unified approach that explains the output of any machine learning model; by delivering cohesive explanations it provides invaluable insight into how predictions are being made and opens up immense possibilities in terms of practical applications. In this tutorial we'll explore how to use SHAP values to explain and improve ML models, delving deeper into specific use cases as we go along.
This article explains how Giskard open-source ML framework can be used for testing ML models and applied to fraud detection. It explores the components of Giskard: the Python library, its user-friendly interface, its installation process, and practical implementation for banknote authentication. The article provides step-by-step guide, code snippets, and leverages the banknote authentication dataset to develop an accurate ML model.
This tutorial teaches you how to upload a PyTorch model (built from scratch or pre-trained) to Giskard, and identify potential errors and biases.
This article provides a step-by-step guide to detecting ethical bias in AI models, using a customer churn model as an example, using the LightGBM ML library. We show how to calculate the disparate impact metric with respect to gender and age, and demonstrate how to implement this metric as a fairness test within Giskard's open-source ML testing framework.
This tutorial teaches you how to build, test and deploy a Huggingface AI model for sentiment analysis while ensuring its robustness in production.
Metamorphic testing are adapted to Machine Learning. This tutorial describes the theory, examples and code to implement it.
Testing the drift of numerical feature distribution is essential in AI. Here are the key metrics you can use to detect it.
Testing drift of categorical feature distribution is essential in AI / ML, requiring specific metrics