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1. Introduction

Coined by Gartner, AIOps—i.e. artificial intelligence for IT operations—is the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows. The IBM Cloud Pak for AIOps is an AIOps platform that deploys advanced, explainable AI using the IT Operations (ITOps) toolchain data so that you can confidently assess, diagnose, and resolve incidents across mission-critical workloads.

IBM Cloud Pak for AIOps eases the path to adopting advanced AI for ITOps to decrease your operational costs. With this Cloud Pak, you can increase your customer satisfaction by proactively avoiding incidents and accelerating your time to resolution.

By integrating multiple separate, manual IT operations tools with into a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with end-to-end visibility and context. It bridges the gap between an increasingly diverse, dynamic, and difficult-to-monitor IT landscape and siloed teams, on the one hand, and user expectations for little or no interruption in application performance and availability, on the other.

Welcome to the Cloud Pak for AIOps Alert Correlation Lab. You will be going through several key exercises that will help you learn key skills around how events can get deduplicated and alerts correlated in order to reduce noise and focus on the most relevant incidents.

Lab Content​

In this Lab, we will focus on different capabilities that help IT Operations personnel with event and alert noise reduction:

  • Event and alert terminology
  • How to load sample event and alert data using a generic webhook and kafka topic
  • Scope-based alert correlation
  • Temporal alert correlation
  • Topological alert correlation

The lab should be executed in the numbered order that you see on the left side of the screen in the navigation pane as sections likely depend on work completed in prior sections.