Technical Case Studies

Real-world examples of how infrastructure professionals are already applying AI in production environments. Each scenario connects the theory from the chapters with hands-on impact and measurable outcomes.

Case 1: Predicting failures with intelligent logs

Scenario: An infrastructure team managed hundreds of VMs and constantly received disk and CPU alerts, often too late to prevent downtime.

Challenge: Logs and metrics existed but provided no predictive signal — alerts only triggered after issues occurred.

Solution:

• Collected logs and metrics using Azure Monitor + Log Analytics

• Integrated Azure Anomaly Detector API to flag abnormal usage trends

• Automated proactive alerts via Azure Logic Apps

Result: ✅ 30% reduction in critical incidents ✅ Improved confidence from stakeholders ✅ Infra team recognized as a predictive operations partner

Lesson: You don’t need to be a data scientist to predict failures — prebuilt AI APIs + clean telemetry are enough to create value.

Case 2: Building an internal copilot with Azure OpenAI

Scenario: A NOC team handled over 200 support tickets weekly — mostly repetitive troubleshooting requests and command lookups.

Challenge: Overload and slow response times, especially off-hours.

Solution:

• Created an internal Copilot using Azure OpenAI (GPT-4)

• Indexed internal documentation and logs

• Connected via Azure Function + Logic Apps + Microsoft Teams bot

Result: ✅ 40% reduction in L1 tickets ✅ 24/7 self-service support ✅ Improved satisfaction and response consistency

Lesson: AI copilots are not just for developers — infrastructure teams can automate support and accelerate resolution.

Case 3: Cost-efficient AI infrastructure for startups

Scenario: A startup wanted to deploy an image classification model trained elsewhere, but lacked GPU expertise and had a limited budget.

Challenge:

• Small team, no MLOps experience

• Need to run inference efficiently and securely

Solution:

• Deployed a Standard_NCas_T4_v3 VM for GPU inference

• Stored model files in Azure Blob Storage

• Used Bicep template for repeatable deployment

• Secured with Azure AD and IP firewall rules

Result: ✅ Total cost under $150/month ✅ Latency under 300 ms ✅ Deployment in under 48 hours

Lesson: With Infrastructure as Code and the right SKU, small teams can run production AI affordably.

Case 4: Scaling GPU workloads with AKS and observability

Scenario: A multinational company ran on-prem GPU servers with local Python scripts — no scalability or monitoring.

Challenge:

• Workloads couldn’t scale across clients

• No fault tolerance or telemetry

Solution:

• Migrated to AKS with a dedicated GPU node pool

• Containerized the model with tolerations + GPU labels

• Added DCGM Exporter + Prometheus + Grafana dashboards

• Enabled autoscaling based on GPU metrics and latency

Result: ✅ 99.9% uptime ✅ 35% faster inference response time ✅ Predictable cost and usage patterns

Lesson: Container orchestration brings enterprise-grade reliability to AI workloads — even for legacy scripts.

Case 5: Using AI to optimize infrastructure costs

Scenario: A SaaS DevOps team needed to cut costs and suspected their AKS cluster was over-provisioned.

Challenge: No visibility into real GPU and CPU utilization — decisions were guesswork.

Solution:

• Combined Prometheus metrics with Azure Cost Management API

• Trained a simple linear regression model in Azure ML

• Built a dashboard showing “optimal vs. current” resource sizing

Result: ✅ 25% monthly savings on compute ✅ Automated idle-node alerts ✅ Data-driven capacity planning

Lesson: Infrastructure + Data + AI = Smarter, measurable cloud efficiency.

“AI doesn’t replace infrastructure — it rewards those who understand it.”