Holistic Observability | Artificial intelligence (AI) | Video Cast | Ennetix
“Holistic Observability” is critical to any enterprise AIOps Initiative. In this Video, Ennetix CEO Prof Bis Mukherjee offers his unique perspectives. This is part of the 3 Videocast series highlighting end-end strategies to take on a robust AIOps implementation – hosted by Chris Clements, A systems engineer with years of experience implementing AIOps.
xVisor for DETERMINISTIC APPLICATION-CENTRIC AIOps
Intelligently infer performance and security deviations, and correlate root causes in real-time using AI/ML powered analytics technologies(ie, AIOps tools). Actionable information provided by the Ennetix AIOps platform helps enterprises to achieve the best-possible user experience of mission-critical applications.
Real-Life Case Studies
Digital IT Enviroment Today
The network and application delivery infrastructure are extremely complex these days, making it a daunting job for enterprise IT organizations and managed services providers that are tasked with managing and maintaining them.
Why then is network downtime – the disruption to business and revenue loss it causes – such a common occurrence even today?
Stitching together multiple legacy solutions
Disjointed triage without predictive outcome guarantee
Lack of unified view for application performance monitoring tools & security insights
Are still using at least one legacy tool
Are using 20 or more tools (including application performance monitoring tools)
Said that AIOps tools are critical for end-to-end visibility
Cite business agility (using AIOps tools) as top driver for change in ITOps
Our customers want to focus on their digital transformation journey, and thus they want their business-critical applications and the underlying network infrastructures to simply work. They challenged us:
Tell me before something impacts or threatens my digital operations
Get me started on the next steps for remediation in parallel
We built xVisor, an AIOps platform, to deliver Deterministic Digital Performance
reduction in overall resolution time by lowering:
MTTA from Hours to Minutes
MTTR from Days to Hours