< Home

ML Monitoring

ML monitoring in machine learning is the method of tracking the performance metrics of a model from development to production. Monitoring encompasses establishing alerts on key model performance metrics such as accuracy and drift.  Initially, the success of such ML projects was dependent on successful model deployment. However, it is important to note that Machine learning models are dynamic in nature - their performance needs to be monitored, or it degrades over time.

ML monitoring helps identify precisely when the model performance starts diminishing, and you can proactively work on resolving it quickly. Monitoring the automated workflows helps to maintain the required accuracy and keeps transformations error-free.

Basic concepts: 

  • Baseline: Users can define the baseline basis on ‘Tag’ or segment of data based on ‘date’. 
  • Frequency: Users can define how frequently they want to calculate the monitoring metrics. 
  • Alerts frequency: Users can configure how frequently they want to be notified about the alerts. 

Page URL copied to clipboard