Tuesday, November 10, 2020
Many of today’s business-essential tasks have become digitized and, as a result, IT teams have had to learn to deal with constant change while ensuring zero downtime. The irony is that, although IT has become business-critical, the productivity and agility of the people building and supporting services behind the scenes has plummeted. Now, companies simply generate too much data for humans to monitor and understand manually, leading to an incredible amount of toil and noise.
We cannot continue to scale monitoring and observability by simply devoting more humans to the task. Artificial Intelligence and Machine Learning have emerged as the cornerstone of a new observability strategy. Using algorithms correctly can eliminate toil, help accelerate the discovery of potential issues across applications and infrastructure, avoid emergencies, maintain agility and ultimately continue delivering innovative business services.
This talk will explain the methods which DevOps practitioners and SREs teams can use to more effectively:
- surfacing important anomalies and events from the deluge of data
- understanding the relationships between alerts
- obtain the context needed to engage the right teams and people
From data discovery to blameless analysis, algorithms automate the cognitive load required by humans to remove the operations toil and continuously assure your customer experience. Learn what DevOps teams can expect from these algorithms and how to apply them.
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