WhyLabs

Generates statistical summaries and profiles of data pipelines using a privacy-preserving approach that processes only aggregated metrics and distributions rather than requiring access to raw training or inference data. The platform computes whylogs-compatible statistical profiles (histograms, cardinality estimates, quantiles) server-side, enabling monitoring without exposing sensitive data to the observability platform.

Monitors statistical distributions of data and model outputs over time, automatically detecting when feature distributions, prediction distributions, or target distributions shift beyond configured baselines using statistical distance metrics (KL divergence, Wasserstein distance, or chi-square tests). Alerts trigger when drift magnitude exceeds user-defined thresholds, enabling proactive model retraining or data investigation before performance degradation occurs.

Monitors large language model outputs for quality, safety, and behavioral anomalies using langkit, an open-source toolkit that computes metrics on LLM responses including toxicity, prompt injection risk, hallucination indicators, and semantic drift. Profiles LLM conversation logs and completions to detect when model behavior deviates from expected patterns, enabling detection of model degradation, jailbreak attempts, or output quality issues.

Continuously monitors statistical profiles and computed metrics against baseline expectations, triggering alerts when anomalies are detected via configured notification channels (Slack, email, webhooks, PagerDuty). Anomaly detection uses statistical methods to identify outliers in metric distributions or sudden changes in trend, with alert severity and routing configurable per metric or data segment.

Defines an open standard and reference implementation (Python/Java SDKs) for computing and serializing statistical profiles of datasets, enabling consistent data profiling across different tools and platforms. Profiles capture distributions, cardinality, quantiles, and custom metrics in a portable format (JSON/protobuf), allowing profiles generated in one system to be consumed by another without vendor lock-in.

Tracks model-specific performance metrics (accuracy, precision, recall, F1, AUC, latency, throughput) over time and visualizes trends to identify performance degradation. Correlates performance metrics with data quality and drift metrics to help diagnose root causes of model degradation, supporting both classification and regression model types.

Computes and tracks data quality metrics (missing values, outliers, schema violations, value distributions, cardinality) for datasets and features over time. Establishes baseline expectations for data quality and alerts when metrics deviate, enabling early detection of data pipeline issues before they impact models.

Analyzes relationships between features and model outputs to identify which features are most important for predictions and how features correlate with each other. Tracks feature importance changes over time to detect when feature relationships shift, indicating potential model retraining needs or data distribution changes.