Superwise is a model observability platform that provides tools for monitoring machine learning systems in production, focusing on metrics for data quality, drift detection, and model performance. It enables organizations to maintain the health of their ML models by offering over 100 customizable metrics and automated monitoring capabilities, ensuring timely detection of issues that could impact model accuracy and reliability.
Funding
$4.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Maintaining the accuracy and reliability of machine learning models in production is challenging due to data drift, performance degradation, and other unforeseen issues. Existing monitoring solutions often lack the flexibility and customization needed to address the unique requirements of different ML use cases. This can lead to delayed detection of problems, impacting model performance and business outcomes.
Solution
Superwise provides a model observability platform that enables organizations to proactively monitor and maintain the health of their machine learning systems. The platform offers a comprehensive suite of tools for tracking data quality, detecting drift, analyzing performance, and ensuring model explainability. With over 100 pre-built and customizable metrics, Superwise allows users to create tailored monitoring policies and receive automated alerts when issues arise. By providing real-time visibility into model behavior, Superwise helps data science teams quickly identify and resolve problems, ensuring the accuracy and reliability of their ML models in production.
Target Audience
Superwise is designed for data scientists, ML engineers, and MLOps teams who need to monitor and maintain the health of machine learning models in production.
Features
- Pre-built and customizable metrics for data quality, drift detection, performance analysis, bias monitoring, and explainability
- Automated monitoring capabilities with customizable policies and notification channels
- Root cause analysis tools to quickly identify and resolve issues impacting model performance
- Explainability features to understand model behaviors at the global, cohort, and individual decision level
- Version comparison to analyze changes between model versions, datasets, and production timeframes
- Anomaly investigation to correlate and group anomalies for faster incident resolution
- Centralized model monitoring management for building segments and managing configurations across multiple models
- Support for monitoring large language models (LLMs) to detect drift, security issues, and privacy violations
- Open-source library in Python for extracting metafeatures from unstructured data