AI/ML for IT Operations - 90‑Day Implementation Playbook (high level)
This targeted article describes a 90-Day Implementation Plan for AI-driven operational improvements to your application operational environments and platforms. We include sample runbooks and code snippets, like Terraform autoscale and AWS Lambda Python snippets. It also covers dashboards, alert rules, and emphasizes AI/ML components like predictive autoscale and AIOps platforms.
90‑Day Implementation Playbook (AI/ML automations)
Weeks 0–2 — Plan & instrument
- Opportunities: Research and become aware of what is possible, what can be accomplished/improved with existing toolsets and platforms, or those that can be readily procured, through their respective AI/ML advancements and capabilities. From here, focus on targeted and measurable Goals.
- Goals: SLA targets, cost reduction %, MTTR target.
- Actions: Deploy agents (Azure Monitor agents / Application Insights; CloudWatch + CloudWatch Agent), centralize logs to Log Analytics / CloudWatch Logs, enable distributed tracing (APM). Baseline collection for 14 days to feed ML models.
Weeks 3–6 — Baseline, dashboards, and thresholds
- Dashboards (examples):
- Service Health: 95th percentile latency, error rate, request rate, instance count.
- Capacity: CPU/memory per instance, queue length, GC pause, disk I/O.
- Cost: spend by resource tag, idle VM hours.
- Alert rules (tiered):
- Warning: 85th percentile CPU > 70% for 10m.
- Critical: 95th percentile latency > SLA or CPU > 90% for 5m.
- Auto‑scale trigger: average CPU > 70% across VMSS/ASG for 5m → scale out; scale in when <40% for 15m.
Weeks 7–10 — AIOps, ML baselining, and predictive autoscale
- AI/ML components: enable predictive autoscale for VM scale sets (Azure Predictive Autoscale) and use ML baselining for anomaly detection rather than static thresholds. In AWS, enable CloudWatch anomaly detection and integrate CloudWatch Investigations for suggested runbooks.
- AIOps platform: ingest alerts/traces into Datadog/Dynatrace + BigPanda for correlation and automated RCA to reduce noise and prioritize incidents.
Weeks 11–13 — Safe automation & runbooks
- Automation pattern: metric → anomaly detection → runbook (automated) → verification → human escalation. Use cooldowns, canary actions, and approval gates.
- Sample runbook (AWS Lambda):
# Lambda: reboot or replace unhealthy EC2
import boto3
ec2 = boto3.client('ec2')
def lambda_handler(event, context):
instance = event['detail']['instance-id']
ec2.reboot_instances(InstanceIds=[instance])
Use CloudWatch Alarm → EventBridge → Lambda → SSM Automation for complex flows.
- Sample Azure Function (auto‑scale webhook):
import logging, requests, os
def main(req):
# call Azure Monitor autoscale REST API or scale VMSS via SDK
return "ok"
- Terraform / ARM snippets: create autoscale setting via azurerm_monitor_autoscale_setting or Microsoft.Insights/autoscalesettings ARM resource; use Terraform AWS autoscaling module for ASG + CloudWatch alarms.
Weeks 14–90 — Optimize, centralize, and govern
- Actions: rightsizing (idle VM reclamation), spot/Reserved instances, central cost dashboard, policy enforcement via IaC. Use AIOps to surface optimization opportunities and run periodic ML retraining for baselines.
Risks, tradeoffs & controls
- Risk: automation causing flapping or unintended scale actions — mitigate with cooldowns, canaries, and manual approval for high‑impact runbooks.
- Risk: poor telemetry → false positives; fix by improving instrumentation and ML retraining.
- Governance: RBAC for runbook execution, audit logs, and staged rollout of automated remediations.
Final recommendation
Start with telemetry and ML baselining, enable predictive autoscale, and add an AIOps correlation layer; pilot on a non‑critical service for 30 days, then expand with runbook automation and cost optimization across Azure and AWS.
Sources:
Autoscale in Azure Monitor - Azure Monitor | Microsoft Learn
Master AWS CloudWatch Auto-Remediation | AIOps Guide | Codez Up
Datadog and BigPanda: Observability and AIOps made better | BigPanda
Azure Monitor Autoscale Setting - Examples and best practices | Shisho Dojo
terraform-aws-modules/autoscaling/aws | complete Example | Terraform Registry
Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence)
