Last week DevOps Institute’s Chief Ambassador, Helen Beal, and Moogsoft’s own Chief Evangelist, Richard Whitehead, continued to follow the exploits of DevOps Engineer Sarah and her journey towards AIOps and Observability enlightenment.
As Helen mentioned at the top, whilst previously focusing on things like anomaly detection and noise reduction, this webcast was going to get a bit deeper under the covers and talk about the nitty-gritty of using AI to ingest and normalize data, including how algorithms help technology teams with de-duplication and correlation and how a tool like Moogsoft provides visualization and promotes collaboration.
As you can see by watching the full webcast, both Helen and Richard are quite excited by the evolution of DevOps, with Helen admitting she “was almost a little bit bored of technology and the IT industry around 10 years ago” until DevOps raised its wild head.
On to Sarah, who starts at her desk in the middle of a sprint review with her remote teammates, slightly befuddled.
She’s saying, “Okay guys we’ve got all this lovely data coming into this cool Observability system. How exactly does it get ingested and normalized?”
That was the first of many questions that arose during the course of the presentation and also dynamically from the live audience. Find highlights below by simply clicking on the links.
Of course, you may review the entire program, which includes several brief demos of Moogsoft, at your leisure here. And please register for the next webinar in this series if you haven’t already:
Q & A
- What kind of data sources are available to the team?
- How does data get ingested?
- How does it get normalized? What does it actually mean?
- What if some type of input data does not have a required field?
- Are your integrations bi-directional?
- What is an algorithm in the context for AIOps?
- How does AIOps speed up MTTR?
- How does correlation work?
- Is correlation glorified grouping?
- Should tags for correlation happen automatically or be set by users?
- How does Moogsoft handle data governance?
