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Predicting and Preventing Service Outage Failures for Real-Time Applications
Live Webcast: February 5, 2020 – BrightTALK
More than 40% of Enterprise customers are “very dissatisfied” with their service’s availability. Thirty-nine percent of outages take up to 12 hours to resolve. Just one hour of downtime for an average-sized enterprise costs $540,000. In this talk, we will start from quantifying the combinatorial complexity of predicting service degradation and/or outages and introduce a fully automated, empirical-driven ML/AI pipeline to discover low-frequency signals leading to future major service problems.
- Concrete examples of how the networking industry is leveraging the latest techniques in ML/AI and workflow automation.
- Advanced de-noising and automated annotation techniques to discover low-frequency signals embedded in a massive amount of input data.
- The importance of empirical data analysis as a precursor to designing the ML algorithm pipeline.