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Simple Log Service:AI machine learning functions

Last Updated:Dec 16, 2024

In machine learning and artificial intelligence (AI), functions refer to the predefined operations or methods used to implement specific algorithms or models. These functions are used in stages such as data preprocessing, feature engineering, model training, evaluation, and prediction. This topic outlines some commonly used machine learning functions in Simple Log Service and their applications.

SQL functions

Description

Vector calculation functions

You can save text, audio, images, and videos in vector databases as vectors to facilitate search and query.

Time series analysis functions

Time series analysis functions are used to process metrics that are generated in Internet service systems or business operations. The metrics can be used to forecast future trends and identify anomalies during operations.

Cluster analysis functions

In business operations, cluster analysis helps identify similar objects such as users, commodities, and markets. You can formulate marketing strategies based on cluster analysis results to improve efficiency and profitability.

Regression analysis functions

Regression models can be used in data analysis, forecasting, automatic monitoring, and anomaly detection. In complex system management, you can use regression models and configure thresholds and alert rules to significantly improve the timeliness and accuracy of problem identification and ensure the stability of your system.

Classification analysis functions

Classification is a machine learning model that you can use to identify the classes of system objects online. For example, you can use the model to identify attack requests, or identify the relationships between elements.

Load balancing measurement function

Before you perform operations to implement load balancing on your distributed system, you must accurately measure the load balancing degree of the system.

Multivariate pattern identification functions

Simple Log Service provides the anomaly detection feature to identify service system anomalies and their root causes. The anomaly detection feature can automatically identify abnormal metric changes based on the current pattern of a metric and machine learning. You can use multivariate pattern identification functions to identify metric patterns.