Operations | Monitoring | ITSM | DevOps | Cloud

The difference between machine learning for business and machine learning for consumers is that for business applications, you must deal with requirements that casual users won’t encounter.

Writing software is hard. Balancing the demands of shipping with assuring quality presents challenges. Also, you must have an eye on keeping code readable and maintainable. It’s not easy. Beyond mere development, maintaining a running application is still harder. Serving users requires responding to problems in a timely fashion. In order to respond to problems, you need to know they exist. In short: writing software is hard; running software is often harder. And that’s why we monitor.

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At Sensu Summit 2019, Adam Westman, Sr. Engineering Manager at Target, introduced us to GoAlert, their on-call scheduling and notification open source project. In this post, I’ll recap his talk, sharing the journey that led them to build GoAlert, the problems they’ve solved, and how they use GoAlert with Sensu Go to simplify monitoring and reduce alert fatigue.

At Sentry, we’re big fans of continuous integration and deployment. We’re also big fans of GitHub — and not just because we employ a number of notable GitHub alumni. We use our own GitHub integration to link issues, identify suspect commits that likely introduced new errors, and suggest assignees who can best resolve each issue. Last month, GitHub released GitHub Actions for general availability.

At AWS re:Invent recently, we excitedly announced Logz.io Infrastructure Monitoring – our new Grafana-based monitoring product! This product is the third pillar of our cloud observability platform, together with Logz.io Log Management and Logz.io Cloud SIEM.