AIOps Discipline

Discipline Track | 6 Modules | ~4 hours total

Overview

AIOps (Artificial Intelligence for IT Operations) applies machine learning to automate and enhance IT operations. While traditional monitoring tells you something broke, AIOps tells you why, predicts what will break next, and can fix problems automatically.

Alert fatigue is real. SRE teams drown in noise while missing critical signals. AIOps applies ML where humans struggle—correlating thousands of events per second, detecting subtle anomalies across thousands of metrics, and predicting failures from historical patterns.

This track covers the complete AIOps journey—from understanding what it is to implementing auto-remediation with safety guardrails.

Prerequisites

Before starting this track:

• Observability Theory Track — Data sources for ML
• SRE Discipline — Incident management context
• MLOps Discipline — ML fundamentals (recommended)
• Basic statistics (mean, standard deviation, distributions)

Modules

#	Module	Complexity	Time
6.1	AIOps Foundations	[MEDIUM]	35-40 min
6.2	Anomaly Detection	[COMPLEX]	40-45 min
6.3	Event Correlation	[COMPLEX]	40-45 min
6.4	Root Cause Analysis	[COMPLEX]	40-45 min
6.5	Predictive Operations	[COMPLEX]	40-45 min
6.6	Auto-Remediation	[COMPLEX]	40-45 min

Learning Outcomes

After completing this track, you will be able to:

1. Understand AIOps maturity — From reactive monitoring to closed-loop automation
2. Implement anomaly detection — Statistical and ML approaches for threshold-free alerting
3. Correlate events — Reduce alert noise through intelligent grouping
4. Perform root cause analysis — Automate the detective work of incident response
5. Predict failures — Forecast problems before they impact users
6. Build auto-remediation — Safe, automated fixes with proper guardrails

Key Concepts

The AIOps Evolution

┌─────────────────────────────────────────────────────────────────┐
│                   OPERATIONS EVOLUTION                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  MANUAL MONITORING (1990s)                                       │
│  ├── Static thresholds                                          │
│  ├── Manual alert triage                                        │
│  └── Reactive fixes                                             │
│                                                                  │
│  ITOM/APM (2000s)                                                │
│  ├── Better visibility                                          │
│  ├── Some correlation                                           │
│  └── Still manual response                                      │
│                                                                  │
│  OBSERVABILITY (2010s)                                           │
│  ├── Metrics, logs, traces                                      │
│  ├── High cardinality data                                      │
│  └── Drowning in data                                           │
│                                                                  │
│  AIOPS (2020s)                                                   │
│  ├── ML-driven detection                                        │
│  ├── Automated correlation                                      │
│  ├── Predictive insights                                        │
│  └── Auto-remediation                                           │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Core AIOps Capabilities

1. Anomaly Detection — Find problems without predefined thresholds
2. Event Correlation — Group related alerts, reduce noise 90%+
3. Root Cause Analysis — Automatically identify probable causes
4. Predictive Analytics — Forecast failures, capacity needs
5. Auto-Remediation — Execute fixes with human oversight

Why Humans Can’t Scale

Challenge	Human Limitation	AIOps Capability
Volume	~100 alerts/day max	Millions of events/second
Speed	Minutes to correlate	Sub-second correlation
Patterns	Misses subtle trends	Detects gradual drift
24/7	Fatigue, context loss	Consistent operation
History	Limited memory	Learns from all incidents

Tools Covered

Category	Tools
Anomaly Detection	Prophet, Luminaire, Datadog Watchdog
Event Correlation	BigPanda, Moogsoft, PagerDuty AIOps
Observability AI	Dynatrace Davis, New Relic AI
Custom Solutions	Python, Kafka Streams, Kubernetes

Study Path

Module 6.1: AIOps Foundations
     │
     │  What AIOps is, maturity model
     ▼
Module 6.2: Anomaly Detection
     │
     │  Statistical vs ML approaches
     ▼
Module 6.3: Event Correlation
     │
     │  Noise reduction, grouping
     ▼
Module 6.4: Root Cause Analysis
     │
     │  Causal inference, blast radius
     ▼
Module 6.5: Predictive Operations
     │
     │  Forecasting, capacity planning
     ▼
Module 6.6: Auto-Remediation
     │
     │  Runbook automation, guardrails
     ▼
[Track Complete] → AIOps Tools Toolkit

Related Tracks

• Before: Observability Theory — Data collection
• Before: SRE Discipline — Operational practices
• Related: IaC Discipline — Infrastructure automation for AIOps platforms
• Related: MLOps Discipline — ML fundamentals
• After: AIOps Tools Toolkit — Hands-on implementations
• After: IaC Tools Toolkit — Automated infrastructure provisioning

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“AIOps isn’t replacing SREs—it’s giving them superpowers.”