Azure ML Resource Optimization
completedSpring 2025
Sponsor
UPS gold
Logistics & Supply Chain
Problem & Approach
- Leveraged Azure Machine Learning to build predictive models estimating resource utilization and response times for high-volume transaction workloads.
- Used model outputs to proactively optimize operational resource allocation (compute, scaling policies, and capacity planning), improving performance and cost-efficiency under peak load conditions.
Technology Stack
Azure MLCloud ComputingOptimizationBig Data
Team
Member 1 Team Lead
Member 2 Developer
Member 3 Developer
Member 4 Developer
Outcome & Impact
35% improvement in resource efficiency through predictive modeling of usage patterns and response times.
Testimonials
"The Azure ML resource optimization project delivered a 35% improvement in resource efficiency. The predictive models for high-volume transaction workloads have direct operational value."