{"id":6294,"date":"2023-01-26T11:29:00","date_gmt":"2023-01-26T18:29:00","guid":{"rendered":"https:\/\/www.tecton.ai\/?p=6294"},"modified":"2023-11-15T13:53:29","modified_gmt":"2023-11-15T20:53:29","slug":"why-monitoring-features-for-real-time-machine-learning-is-challenging","status":"publish","type":"post","link":"https:\/\/www.tecton.ai\/blog\/why-monitoring-features-for-real-time-machine-learning-is-challenging\/","title":{"rendered":"Challenges of Feature Monitoring for Real-Time Machine Learning"},"content":{"rendered":"\n


To be successful with machine learning, you need to do more than just monitor your models at prediction time. You also need to monitor your features and prevent a \u201cgarbage in, garbage out\u201d situation. However, it\u2019s extremely hard to detect problems with the data being served to your models. This is especially true for real-time production ML applications like recommender systems<\/a> or fraud detection systems<\/a>. In this post, we’ll explore what feature monitoring for real-time machine learning entails and the common obstacles you will face. (Stay tuned for Part 2, where we will dive into how Tecton can help you solve some of these challenges.)<\/em><\/p>\n\n\n\n

<\/span>What is feature monitoring for machine learning?<\/span><\/h2>\n\n\n\n

In machine learning, a feature is an input signal to a predictive model. Typically, a feature is a transformation on raw data. While it is important to monitor the raw data that is used to create features, it is even more critical to monitor the feature values after they have been transformed, as this is the data that the model will actually use.<\/p>\n\n\n\n

\"Diagram
Raw event data is transformed into features. To monitor features, you want to be able to observe and track the feature values post-transformation.<\/figcaption><\/figure>\n\n\n\n

Feature monitoring can be grouped into two classes: Monitoring features at the value\/row level, or monitoring the aggregations of features, referred to as either metrics or statistics.<\/p>\n\n\n\n

Monitoring individual feature values<\/h3>\n\n\n\n

The following are examples of monitoring that can be performed at the value or row level for machine learning features:<\/p>\n\n\n\n