Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.
In this video, we continue our series on implementing AIOps using the Elastic Stack. We recap the process of collecting logs, metrics, and traces from 11 microservices running in a Kubernetes cluster and show how we can use this data to reduce noise and extract relevant information. We use the Elastic Stack search function to filter through 36 million log lines and demonstrate how field statistics can provide insights into the types of services and messages being logged. This video is useful for anyone looking to implement AIOps and improve their log management using the Elastic Stack.
00:00 – Noise Reduction introduction
01:45 – Discover and Logs
04:28 – Searching on Logs with Elastic
06:21 – Automatic Field Statistics
10:47 – Explain Log Rate Spikes
14:25 – Root Cause of a log spike
14:55 – Log pattern analysis
– Learn why Elastic Observability was recognized as a Strong Performer in the Forrester Wave AIOps, Q4 2022 report: https://www.elastic.co/explore/devops-observability/forrester-research-wave-aiops-report
– Learn more about Elastic Kibana: https://www.elastic.co/kibana/
– Learn more about AIOps: https://www.elastic.co/observability/aiops
– Learn more about Log Monitoring: https://www.elastic.co/observability/log-monitoring
– Learn more about APM: https://www.elastic.co/observability/application-performance-monitoring
– Learn more about Elastic Observability: https://www.elastic.co/observability/
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You can watch this video also at the source.