Machine learning model failures can cause serious damage to your business and the reputation of the company
Act proactively before models hamper the business and always know the state of your ML infrastructure
Build trust in your ML models by understanding the inner working and unravel the mystery of black-box models
Machine Learning model bias can lead to bad reputation and GDPR, CCPA expects you to explain each predictions to your customers
Reactive measures will take lot more time to figure out how, where and when model failed. Avoid this by being proactive in monitoring.
Track everything related to your machine learning model in one place
Capture prediction along with model features to find the drifts
Capture feedback to understand the model accuracy in production
Observe time taken for each inference to have good user experience
Track cpu, memory and disk usage of the ML infrastructure
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
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
Quality of your input data might drop because of change in the upstream pipeline. Keep track of data integrity and anomalies.
Machine Learning models can easily become biased towards certain segment of the population if the data is unbalanced.