The Basics of AI for IT Operations Management

1. The Basics of AI for IT Operations Management AI for IT Operations Management (AIOps) is a combination of Big Data, Artificial Intelligence (AI) and Machine Learning (ML) which is used to enhance the IT Operations. Points for CXOs to access AIOps tool: - Rates of raw ingestion: The data collection speed is unravelled here from different nodes. Latency of transport Rate of data flow to distributed database I/ O Mb/ Sec Rate of Data Processing: It is done for the purpose of measuring consumption. Query Latency: This needs to be monitored in the database Rates of Pattern Learning: Time of Pattern Recognition Effectiveness of Pattern Recognition Rate of anomaly detection: No of anomalies detected/ unit time: This measures the number of anomalies which are detected by AIOps. Anomaly co-relation with event data: For contextualizing and effectiveness, the anomaly information’s are correlated with event data and other sources of metrics. Data Utilization: Data from different IT systems and sources are incorporated here from ITOM data, application development tools etc. Support for wide variety of data Integration with automation tools: It is crucial to integrate the AIOps tools and the automation tools. The existing platforms like the application release automation tools, - - - - - - - 1

2. IT process automation tools, and continuous configuration and automation tools are critical to automate. Training required: This includes the cost of the company which is required to train the employees to use the AIOps tools. Cost of Implementation: This is one-time implementation cost. Availability of SaaS solutions: SaaS based solutions are preferred over on-premise solutions Manual Intervention: To improve algorithms, what amount of manual intervention is required? Adopting one or more AIOps solutions: There are companies which use more than one AIOps solution. However, a single platform is advisable. - - - - - AIOps Platforms: - Applications Development tools data Source code management Bug tracking and testing ITOM Data: Service Desk Agents Discovery Sates Documentation Mechanisms Automation Artifacs Configuration I & O and Application Operational Data Streams Logs Packets Flows Public and Private Content Sources: Government/Nonprofits Commercial Data Providers Consumer Applications Other Tool Data Sources: LOB Applications Social Media IoT IAM - - - - The above article has been sourced from Zero Incident Framework (ZIF) who provides the best AIOps based Analytics Platform and the best AIOps solutions in USA. 2