#monitoring

Evidently is an open-source framework built to evaluate, test, and monitor both predictive and generative ML and LLM systems. It provides over 100 metrics, including those for data drift detection, classification, and regression. The tool supports exploratory analysis and real-time monitoring with outputs in formats like JSON and HTML. Its modular architecture allows for custom metrics integration with existing data pipelines. Available as a self-hosted or cloud service, it includes features such as user management and alerting.

Grafana Status Panel is a plugin for centralized component monitoring, featuring customizable severity markers and disable settings. It allows visualization of multiple metrics with threshold-based alerts and offers regex for specific data display. The plugin integrates seamlessly with Grafana, working efficiently with InfluxDB and Graphite data sources.

Monitor deep learning experiments and hardware usage with ease using this open-source tool. Seamlessly integrate with minimal code to track important experiment details including configurations and hyperparameters. The API offers options for custom visualizations and efficient logging, making it a comprehensive solution for managing experiments from mobile devices or laptops. Simple installation and server setup allow you to begin tracking and evaluating your machine learning projects without hassle.

Discover essential tools for Amazon DocumentDB, offering solutions for migration, performance improvement, and monitoring. The DocumentDB Index Tool assists in index-only migrations, while the Compatibility Tool identifies unsupported queries in Amazon DocumentDB. Global Clusters Automation aids in managing global clusters for Disaster Recovery and Business Continuity. These resources, curated by Amazon DocumentDB specialists, offer enhanced functionality and reliability but lack official AWS support. Suitable for developers aiming to optimize DocumentDB operations.