Anomaly detection overview

Anomaly detection is a data mining technique that you can use to identify data deviations in a given dataset. For example, if the return rate for a given product increases substantially from the baseline for that product, that might indicate a product defect or potential fraud. You can use anomaly detection to detect critical incidents, such as technical issues, or opportunities, such as changes in consumer behavior.

One challenge when you use anomaly detection is determining what counts as anomalous data. If you have labeled data that identifies anomalies, you can perform anomaly detection by using the ML.PREDICT function with one of the following supervised machine learning models:

• Linear and logistic regression models
• Boosted trees models
• Random forest models
• Deep neural network (DNN) models
• Wide & Deep models
• AutoML models

If you aren't certain what counts as anomalous data, or you don't have labeled data to train a model on, you can use unsupervised machine learning to perform anomaly detection. Use the ML.DETECT_ANOMALIES function with one of the following models to detect anomalies in training data or new serving data:

Recommended knowledge

By using the default settings in the CREATE MODEL statements and the inference functions, you can create and use an anomaly detection model even without much ML knowledge. However, having basic knowledge about ML development helps you optimize both your data and your model to deliver better results. We recommend using the following resources to develop familiarity with ML techniques and processes:

• Machine Learning Crash Course
• Intro to Machine Learning
• Intermediate Machine Learning