whylogs is an open source standard for data and ML logging
whylogs logging agent is the easiest way to enable logging, testing, and monitoring in an ML/AI application. The lightweight agent profiles data in real time, collecting thousands of metrics from structured data, unstructured data, and ML model predictions with zero configuration.
whylogs can be installed in any Python, Java or Spark environment; it can be deployed as a container and run as a sidecar; or invoked through various ML tools
Accurate data profiling: whylogs calculates statistics from 100% of the data, never requiring sampling, ensuring an accurate representation of data distributions
Lightweight runtime: whylogs utilizes approximate statistical methods to achieve minimal memory footprint that scales with the number of features in the data
Any architecture: whylogs scales with your system, from local development mode to live production systems in multi-node clusters, and works well with batch and streaming architectures
Configuration-free: whylogs infers the schema of the data, requiring zero manual configuration to get started
Tiny storage footprint: whylogs turns data batches and streams into statistical fingerprints, 10-100MB uncompressed
Unlimited metrics: whylogs collects all possible statistical metrics about structured or unstructured data
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