Model evaluation and model monitoring are not the same thing. They may sound similar, but they are fundamentally different. Let’s see how.
Data monitoring
From missing values to dataset completeness, and population decay. Everything you'll need to ensure that your models have what they need to make their best predictions
Data quality
I want to monitor missing values for all entities in all LTV models across VIP customers
With data quality monitors your team can quickly uncover data pipeline issues and detect when features, predictions, or actual data points don’t conform to expectations.
Dataset schema
I want to monitor removed fields for S3 in 3 models across entire set
Easily secure the structure of your training to inference schema across datasets, data connectors, and streaming to make ensure consistent tracking and visibility of your data and machine learning processes.
Data activity
I want to monitor model stillness for prediction in 3 models across all segments and entire set
Measure the activity levels of your ML models and their operational metrics to catch in real-time variances potentially correlated with model issues and technical bugs.
Population changes
I want to monitor growing predictions in churn models across NA & EU accounts
Stay on top of population representation to achieve the best results for all sub-populations your model serves with monitors to identify growing, decaying, and under-performing segments.
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Featured resources
Data drift detection basics
Drift in machine learning comes in many shapes and sizes. Although concept drift is the most widely discussed, data drift is the most frequent, also known as covariate shift. This post covers the basics of understanding, measuring, and monitoring data drift in ML systems. Data drift occurs when the data your model is running on
5 ways to prevent data leakage before it spills over to production
Data leakage isn’t new. We’ve all heard about it. And, yes, it’s inevitable. But that’s exactly why we can’t afford to ignore it. If data leakage isn’t prevented early on, it ends up spilling over into production, where it’s not quite so easy to fix. Data leakage in machine learning is what we call it
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