What you will find in Mercury
Mercury is structured with multiple micro-repositories in a highly modular design. Each repository is independent, but some have interdependencies. For example, mercury-robust uses mercury-dataschema and mercury-monitoring underneath.
Utility package that automatically infers feature types and calculates different statistics based on those types, given a Pandas DataFrame. It is very useful for validating whether different datasets match the same schema, or for using their statistics to calculate drift.
Offers a collection of methods and techniques to interpret and inspect ML models. This package focuses on providing explanations for classification and regression models, both locally and globally, to gain a better understanding of how a Machine Learning model works and the factors that contribute to its predictions.
Package dedicated to model monitoring. It's crucial to continuously monitor the performance of ML models in production. This involves detecting changes in the incoming data distribution or data drift and estimating the model accuracy at inference time.
A library to analyze sequences of events extracted from transactional data. These events can be automatically discovered or manually defined.
A lightweight framework for performing robust testing on ML models and datasets. It ensures that data workflows and models are robust against certain conditions, such as data drift, label leaking, or input data schema issues, by raising an exception when they fail.
A C++ library for creating, updating, and querying SetTrie objects. A SetTrie is a container of sets that performs efficient subset and superset queries.