Open Source ML Observability Platform
Observe, Monitor and Explain Production AI So that you can prevent the inevitable model degradation.
Monitoring Across the ML Workflow
Benifits get by ML Engineers & Product Leaders
Gain Production Insight
With Waterdip platform ML Engineers and Data Scientists can check their Model's performance across a wide range of KPI and measurements in a single place
Faster Troubleshooting
Debugging ML system is really cumbersome process. With much needed production insight, it becomes much easier to troubleshoot production issues
Increase Productivity
With the power of continuous production insight and easier troubleshooting, Data Scientists saves more time, so that they can work on something awesome
Gain Transparency
With the combined view of model performance and explainability of model's decision making process, product leaders gain complete transparency of their AI Systems
Increase Trust
The transparency increases the trust of product leaders to their AI systems, as they know their AI system are making decision with true responsibility
Build Confidence
Transparency and Trust gives product leaders more confidence to build and experiment with more ground braking ideas, to make higher impact to business KPIs
Everything data science and ML teams need to stop firefighting
ML Performance Tracing
Observer ML model's performance is one of the key activity needs to be done in production AI system. With waterdip platform one can have the continuous observability of model's performance matrices like Confusion Matrix, Moving Accuracy, AUC/ROC, Gini, Precision/Recall etc.
Outlier detection
New environment is full of unexpected inputs, that can cause outlier for predicted data. With waterdip finding and identifying outlier in data or prediction become seamless, so troubleshooting becomes easy for the ML engineers and Data Scientists
Drift detection
Due to this drift, the model keeps becoming unstable and the predictions keep on becoming erroneous with time. With waterdip platform we can detect drift in Data and ML model.
Proactive alerts
Anticipate what is changing and how it impacts your business and surface the right alert to the right people, so can action can be taken promptly
Model Prediction Explainer
Understand how the ML Model is taking decision under the Black Box, so that it can be easily detected why any model is making wrong decision and can be fixed sooner
