What is AIOps?
The AI behind Autonomous Plants and Critical Infrastructure Sleep comfortably thanks to AIOpsWelcome to AIOps, Artificial Intelligence for IT Operations. Collect Ingests everything from logs, metrics, events not just monitors but also extracts context. Learn Learns what is "normal" or what is "abnormal" with ML. Guess AI sees this and warns in advance, for example: "This CPU spike is causing this service to crash after 30 minutes every time." Automate Doesn't create alerts from logs only takes action: Restart, scale, rollback. Whatever is needed and no human needed. DevOps was cute. GitOps was clean. Then infra started scaling like hell, sensors started screaming and logs became novels. Now, it's AIOps time! No more manual dashboards at 4AM, no more "wait, which pod is on fire?" with AIOps, we're not just monitoring we're teaching the system to think, learn and action. Autonomously! So last night irolled out our AIOps module for mission-critical container workloads at Goosey Inc. and i thought it might just run silently overnight. Turns out it kept me awake and not from errors but from excitement. At Goosey, we are building next-gen observability for data centers, powered by real-time sensor analytics and AI and from anomaly detection to early warning systems Goosey Inc. is turning noise into insight. From anomaly detection to early warning systems, turning noise into insight. Stay tuned, something nuclear is coming :) Technologies Stack: Machine Learning Log & Metric correlation engines Natural Language Processing Automation tools Core Stack (The AIOps Skeleteon): Data Ingestion Prometheus → metric Loki Fluentd OpenTelemetry Data Store Elasticsearch InfluxDB NATS.IO Correlation Grafana Machine Learning Yelp's ElastAlert OpenObserve AI & ML Layer: Facebook's Kats LangChain NVIDIA Morpheus Automation Layer: Terraform or Pulumi k8s Operators n8n Observability: Grafana Kibana PagerDuty Scenario: 04:00 AM, CPU spike and memory leak! So AI scans old data and observability records, sees what caused the crash before, finds root cause and restarts container, pods or applications. And writes note on Dashboard: "incident auto-resolved. go back to sleep.
The AI behind Autonomous Plants and Critical Infrastructure
Sleep comfortably thanks to AIOpsWelcome to AIOps, Artificial Intelligence for IT Operations.
Collect
Ingests everything from logs, metrics, events not just monitors but also extracts context.Learn
Learns what is "normal" or what is "abnormal" with ML.Guess
AI sees this and warns in advance, for example:
"This CPU spike is causing this service to crash after 30 minutes every time."Automate
Doesn't create alerts from logs only takes action:
Restart, scale, rollback. Whatever is needed and no human needed.
DevOps was cute. GitOps was clean. Then infra started scaling like hell, sensors started screaming and logs became novels.
Now, it's AIOps time!
No more manual dashboards at 4AM, no more "wait, which pod is on fire?" with AIOps, we're not just monitoring we're teaching the system to think, learn and action. Autonomously!
So last night irolled out our AIOps module for mission-critical container workloads at Goosey Inc. and i thought it might just run silently overnight. Turns out it kept me awake and not from errors but from excitement. At Goosey, we are building next-gen observability for data centers, powered by real-time sensor analytics and AI and from anomaly detection to early warning systems Goosey Inc. is turning noise into insight. From anomaly detection to early warning systems, turning noise into insight.
Stay tuned, something nuclear is coming :)
Technologies Stack:
Machine Learning
Log & Metric correlation engines
Natural Language Processing
Automation tools
Core Stack (The AIOps Skeleteon):
Data Ingestion
Prometheus → metric
Loki
Fluentd
OpenTelemetryData Store
Elasticsearch
InfluxDB
NATS.IOCorrelation
Grafana Machine Learning
Yelp's ElastAlert
OpenObserve
AI & ML Layer:
Facebook's Kats
LangChain
NVIDIA Morpheus
Automation Layer:
Terraform or Pulumi
k8s Operators
n8n
Observability:
Grafana
Kibana
PagerDuty
Scenario: 04:00 AM, CPU spike and memory leak! So AI scans old data and observability records, sees what caused the crash before, finds root cause and restarts container, pods or applications.
And writes note on Dashboard:
"incident auto-resolved. go back to sleep.
