Search Results for "drift"

NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. ...

whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond...

Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve.

This is a Java implementation of WhyLogs, with support for Apache Spark integration for large scale datasets. Understanding the properties of data as it moves through applications is essential to keeping your ML/AI pipeline stable and improving your user experience, whether your pipeline is built for production or experimentation. WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log...

...You describe system intent in Ruby-based “recipes” and “cookbooks” made of resources (packages, files, services, users, registries, and more) that converge idempotently, so repeated runs only change what’s necessary. Its client regularly gathers system facts, evaluates policy, and enforces drift correction, which makes environments reproducible from bare metal to cloud instances and containers. Chef’s ecosystem includes roles, environments, and Policyfiles to scope configuration for different stages and teams, plus handlers and notifications for operational visibility. It integrates with secrets stores, package managers, and service managers on Linux and Windows, while custom resources let you encapsulate domain-specific logic cleanly.

Now maintained at github.com/spacepy/spacepy Space Science library for Python - contains superposed epoch classes, drift shell tracing, access to magnetic field models, streamline tracing, bootstrap confidence limits, time and coordinate conversions, etc.

Arrowmatcher is a ballistics software for crossbow archers. It can calculate the speed, kinetic energy, trajectory, aiming solution and much more of an arrow (or bullet). It uses a simplified 6 degree of freedom model.