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Log Analysis with Machine Learning: An Automated Approach to Analyzing Logs Using ML/AI

May 20, 2021 | David Gildeh

When a new/unknown software problem occurs, chances are an SRE or developer will start by analyzing and searching through logs for root cause - a slow and painful process. So it's no wonder using machine learning (ML) for log analysis is getting a lot of attention. 

When a new/unknown software problem occurs, chances are an SRE or developer will start by analyzing and searching through logs for root cause - a slow and painful process. So it's no wonder using machine learning (ML) for log analysis is getting a lot of attention. 

 

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Using Autonomous Monitoring with Litmus Chaos Engine on Kubernetes

March 6, 2020 | David Gildeh

A few months ago, our friends at Maya Data joined our private Beta to give our Autonomous Log Monitoring platform a test run. During the test, they used their newly created Litmus Chaos Engine to generate issues in their Kubernetes cluster, and we managed to detect all of them successfully using our Machine Learning completely unsupervised. Needless to say, they were impressed!

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The Future of Monitoring is Autonomous

March 1, 2020 | David Gildeh

Monitoring today is extremely human driven. The only thing we’ve automated with monitoring to date is the ability to watch for metrics and events that send us alerts when something goes wrong. Everything else: deploying collectors, building parsing rules, configuring dashboards and alerts, and troubleshooting and resolving incidents, requires a lot of manual effort from expert operators that intuitively know and understand the system being monitored.

TL;DR

Monitoring today puts far too much burden on DevOps and developers. These teams spend countless hours staring at dashboards, hunting through logs, and maintaining fragile alert rules. Fortunately, unsupervised machine learning can be applied to logs and metrics to autonomously detect and find the root cause of critical incidents. Read more below, or start using our Autonomous Monitoring Platform for free - it takes less than 2 minutes to get started.  

Introduction

Monitoring today is extremely human driven. The only thing we’ve automated with monitoring to date is the ability to alert on rules that watch for specific metrics and events that occur when something known goes wrong. Everything else - building parsing rules, configuring and maintaining dashboards and alerts, and troubleshooting incidents - requires a lot of manual effort from expert operators that intuitively know and understand the system being monitored.

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