WhyLabs: Observe
Keep an eye on your AI!
Full AI lifecycle observability for insights into your data and model health, alerting you to drift events, performance degradations, potential attacks, and model behavior changes.
Thousands of users love and trust WhyLabs:
Monitor
Whether you are deploying batch inference models or a live prediction service, we provide infrastructure agnostic and real-time telemetry metrics for immediate anomaly detection and monitoring of drift, data quality issues, and performance degradation. With WhyLabs, teams have decreased Time to Resolution of AI issues by 10x.
Prevent
Predictive models drift, decay in performance, and exacerbate bias. With WhyLabs, teams can prevent these undesirable outcomes from hurting the ROI. Teams use WhyLabs to automate model operations: retraining when training-serving skew is detected, pausing pipelines when data quality issues are detected, and quarantining the model in case of growing bias.
Automate
Once teams get models to production, operations become a heavy lift for the ML team - blocking innovation. Homegrown tools require expensive maintenance and struggle to keep up with the needs of the ML team. With WhyLabs, ML organizations have been able to automate more than 80% of manual work associated with running AI in production, freeing time to launch optimized models, faster.
Observability is table stakes for production AI
Monitoring purpose-built for AI applications
WhyLabs Observe delivers the most comprehensive set of capabilities for catching drifts, data quality issues, and model performance degradations. The most popular features are:
- Massively scalable telemetry profiling
- Automatic model onboarding
- Smart, zero-config monitoring set-up
- Templatable configurations to drive standards across teams
- Debugging workflows for feature drift, performance segmentation, and bias tracing
- Customization of dashboards and reports
The most reliable and cost-effective observability
WhyLabs AI Control Center offers a unique architecture that allows customers to switch on observability for 100% of the data, never sampling because sampling distorts the distributions and causes high rate of false alarms. Customers configure monitoring by connecting WhyLabs directly to training and inference data, in batch or real time. This integration is cost effective and privacy-preserving, making it the best choice in high inference volume AI applications for organizations such as Healthcare and FinTech.
Observe any model, on any infrastructure, at any scale
The hard work begins once the ML application is deployed to production. Operating the ML application means creating feedback loops that inform various stakeholders of the health and quality of the data, model, and predictions.
These feedback loops collect the data necessary to ensure that the model's performance and customer experience do not degrade over time. Without proper tools, monitoring, and analyzing feedback data takes about 40% of the team's daily effort. Let us remove the burden of monitoring so that your teams can focus on innovation.
