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Proactive ML Model and Data Monitoring
Proactive ML Model and Data Monitoring

Proactive ML Model and Data Monitoring

Monitor production machine learning model predictions and get alerted on performance decay, data anomalies and data drift

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Drive Business with Confidence in ML Systems

Machine learning model failures can cause serious damage to your business and the reputation of the company 

Revenue

Potential revenue loss when model fails to predict or predicts wrong

Decision

Your data-driven decision can be wrong as the model might not be giving the right answer

Compliance

Your models might be biased towards certain segment of the population unknowingly

Build Your Business With Strong ML Foundation

Business Continuity

Act proactively before models hamper the business and always know the state of your ML infrastructure

Trust and Confidence

Build trust in your ML models by understanding the inner working and unravel the mystery of black-box models

Regulatory compliance

Machine Learning model bias can lead to bad reputation and GDPR, CCPA expects you to explain each predictions to your customers

Enhance productivity

Reactive measures will take lot more time to figure out how, where and when model failed. Avoid this by being proactive in monitoring.

Single Place to Track any Production Model

Track everything related to your machine learning model in one place

Predictions

Capture prediction along with model features to find the drifts

Ground Truth

Capture feedback to understand the model accuracy in production

Response Time

Observe time taken for each inference to have good user experience

System Usage

Track cpu, memory and disk usage of the ML infrastructure

Monitor Everything

Prediction Accuracy
Prediction Accuracy

Compare model predictions with ground truth to track metrics like Accuracy, F1-score, MAE, RMSE, R-squared etc. Get alerted on performance decay to trigger actions before it causes serious damage

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Data Drift
Data Drift

Proactively identify inconsistent, missing, bad data and ensure they don't impact your models. Monitor changes in statistical properties of your data to detect Data Drift

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Data Quality
Data Quality

Quality of your input data might drop because of change in the upstream pipeline. Keep track of data integrity and anomalies.

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Model Bias
Model Bias

Machine Learning models can easily become biased towards certain segment of the population if the data is unbalanced.

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Machine Learning Monitoring product

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