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Use AIOps to Improve Uptime and Reduce Mean Time to Repair

Paige Roberts  (Open Source Relations Manager, Vertica)

Pass Type: All Access

Track: IoT, Smart-X and AI, System Resiliency

Format: Power Session

Vault Recording: TBD

Hardware breaks. While a lot of industries talk about going to the cloud – aka someone else’s hardware – to solve uptime issues, this doesn’t work so well when you ARE someone’s hardware provider. Whether you’re a telecom company with towers and networks, a computer infrastructure company, an energy utility, a manufacturing company, or even a car company – anyone who provides solid metal hardware needs to make sure it keeps running.

AIOps is the smart application of advanced analytics including machine learning to monitor, and in many cases fix IT related problems before the end user is even aware of them. A good example has embedded analysis capability that can improve the productivity of support technicians. A wide variety of industries are getting similar benefits by embedding the ability to analyze IoT data. Telecom is another industry with a constant need to monitor traffic, automatically identify overloaded areas, and reroute calls to prevent outages or dropped calls. Where should your next tower go? How can the flood of 5G data be captured, managed, and put to use, without overloading staff or technology?

In every industry, the key question is: How can I find and fix issues before anyone is aware of the problem, or at least vastly reduce the mean time to repair those issues that do occur? AIOps holds the key. Harness the flood of IoT data to catch and prevent incidents before they happen or fix them as soon as possible after they happen.

This sounds amazing as a concept, but how do companies implement this? What is involved? What gotchas hide in the details? With examples from leaders in multiple industries, this presentation will help you learn:
  • What is AIOps and what can you expect from a solid implementation
  • Benefits AIOps provides in various use cases: predictive maintenance, performance optimization, network bottleneck identification and remediation, MTTR reduction, etc.
  • Example implementations - problems you are likely to run into, requirements to make it work, tradeoffs you need to consider, etc.