ML-Monitoring-for-Attrition-Risk-Assessment-System

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

Algorithms for outlier, adversarial and drift detection

A crowdsourced distributed cluster for AI art and text generation

A Neo4j movies React application with backends in Python/Flask and Node/Express.

最强接口测试平台

Hands-on Microservices with Python [ video], published by Packt

?‍♀️ Query spaCy's linguistic annotations using GraphQL

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

A line-based framework to detect and extract tabular data in JSON format from raster images using computer vision and Tesseract OCR.