Auto-Scale Infrastructure → Monitor Performance → Alert Teams
Automatically scale cloud infrastructure based on AI workload demands and notify teams of performance changes in real-time.
Workflow Steps
AWS Auto Scaling
Monitor and scale resources
Configure Auto Scaling Groups to monitor CPU, memory, and custom AI workload metrics. Set scaling policies to automatically add/remove instances when thresholds are met (e.g., >70% GPU utilization triggers scale-up).
AWS CloudWatch
Collect performance metrics
Set up CloudWatch dashboards to track key metrics like inference latency, throughput, error rates, and resource utilization across your AI infrastructure. Create custom metrics for ML model performance.
PagerDuty
Route alerts to on-call teams
Configure PagerDuty to receive CloudWatch alarms and route them to appropriate engineering teams based on severity. Set up escalation policies for critical AI service failures.
Slack
Send team notifications
Use PagerDuty's Slack integration to automatically post scaling events and performance alerts to relevant team channels. Include context like which models are affected and current load metrics.
Workflow Flow
Step 1
AWS Auto Scaling
Monitor and scale resources
Step 2
AWS CloudWatch
Collect performance metrics
Step 3
PagerDuty
Route alerts to on-call teams
Step 4
Slack
Send team notifications
Why This Works
Combines infrastructure automation with intelligent alerting to ensure AI services remain performant while keeping teams informed without manual monitoring
Best For
Large organizations running AI workloads that need automatic scaling and team coordination
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