What is a data drift?
Data Drift Defined Data drift is unexpected and undocumented changes to data structure, semantics, and infrastructure that is a result of modern data architectures. Data drift breaks processes and corrupts data, but can also reveal new opportunities for data use.
What is data drift detection?
What is data drift? Data drift is one of the top reasons model accuracy degrades over time. For machine learning models, data drift is the change in model input data that leads to model performance degradation. Monitoring data drift helps detect these model performance issues.
What is Concept drift in data stream mining?
The ability to detect and adapt to changes in the distribution of examples is paramount for data stream mining algorithms. The shift in the underlying distribution of examples arriving from a data stream is referred to as concept drift. Concept drift occurs over time and the rate at which the drifts occurs varies.
How do I know what model my drift is?
- Step 1: Quickly identify prediction drift in real-time model outputs as compared to a training or baseline set complimenting JS-Divergence with your domain knowledge on drift thresholds on production data.
- Step 2: Drill down into the selected time window to view drift in underlying features, again using JS-Divergence.
How to detect data drift and concept drift?
Since both drifts involve a statistical change in the data, the best approach to detect them is by monitoring its statistical properties, the model’s predictions, and their correlation with other factors. For example, you could deploy dashboards that plot the statistical properties to see how they change over time.
Why does data drift in a machine learning model?
The reason for this is not to do with the machine learning model itself, but the data distributions. There is a shift that occurs at the data level, as the distribution of the data used to train an ML model, called the source distribution, is different from the distribution of the newly available data, the target distribution.
How does the data drift algorithm work in azure?
The data drift algorithm provides an overall measure of change in data and indication of which features are responsible for further investigation. Dataset monitors produce a number of other metrics by profiling new data in the timeseries dataset.
How is Adwin used to detect data drift?
Adaptive Windowing (ADWIN): ADWIN (ADaptive WINdowing) is an adaptive sliding window algorithm for detecting change, and keeping updated statistics about a data stream. ADWIN allows algorithms not adapted for drifting data, to be resistant to this phenomenon.