AI-ML Functionality Overview in Arbutus

This document is a guide that details the AI-ML functionality available in Arbutus. It introduces various AI-ML applications such as Outliers, Clusters, Sentiment Analysis, and Data Categorization using ChatGPT. The guide explains how these applications utilize AI and machine learning algorithms to analyze large datasets efficiently, identifying patterns, trends, and anomalies that may indicate potential risks. The document outlines the importance of handling outliers, including their definition, causes, and the necessity of their removal for accurate data analysis. It presents different statistical methods for identifying outliers, such as the Z-Score, Modified Z-Score, and Interquartile Range methods. Additionally, it describes how to use Arbutus technology to implement these methods, including the steps to follow within the Arbutus interface. The guide emphasizes that all machine learning capabilities are integrated within Analyzer Procedures, ensuring data remains on customer premises, and provides instructions for configuring AI capabilities with ChatGPT/OpenAI accounts.