AIOps Learning Roadmap
Stage 1: Foundation — Refresh Your Core Goal: Understand how AIOps extends traditional DevOps/CloudOps. Learn: What is AIOps? Key capabilities: anomaly detection, event correlation, root cause analysis, predictive alerts, automation. Gartner’s 3 pillars: Monitoring + Machine Learning + Automation . Differences between DevOps, MLOps, and AIOps. AIOps Lifecycle: Data collection → Normalization → Analysis → Correlation → Automation → Continuous Learning. Hands-on: Explore AIOps case studies (e.g., ServiceNow, Dynatrace, Moogsoft, Datadog). Read whitepapers by Gartner or IBM on “The Evolution of AIOps.” Stage 2: Data & Observability Foundation Goal: Learn how to collect and process data for AI-driven insights. Learn: Telemetry Data Types: Metrics, logs, traces, events. Observability Stack: Prometheus, Grafana, Loki, OpenTelemetry, Elasticsearch, Fluentd, Kibana (EFK), Jaeger. Data Pipelines for AIOps: Kafka, Spark, or Flu...