The Serverless ML Inference Service is an advanced cloud computing project that combines Inference endpoints and Model monitoring, built with S3. The project follows a cloud-native architecture where resources are provisioned, scaled, and managed as code, making it reliable, cost-effective, and easy to reproduce. It showcases professional cloud engineering techniques while delivering a complete, demo-ready platform.
Managing this workload on traditional infrastructure is slow, expensive, and hard to scale. Without a cloud platform built on Model monitoring and S3, there is no automated, resilient, and pay-per-use way to run the service reliably for growing demand.
This project applies advanced cloud engineering practices through Inference endpoints, orchestrated with S3 and Model monitoring. The system is designed for automation, observability, and cost control, with security and resilience built in. It delivers consistent, scalable results and can be extended to additional cloud services and regions.
S3
Docker
AWS / Azure / GCP
Docker / Kubernetes
Terraform / IaC
Serverless services
Monitoring and CI/CD
SageMaker / Vertex AI
Lambda
MLflow
Cloud-native system with Inference endpoints and Model monitoring
Automated provisioning and scaling on demand
Observability with metrics, logs, and alerts
Security controls, encryption, and access management
Reusable cloud modules for related features
Cost-aware, documented, maintainable cloud architecture
The system follows a cloud-native architecture: the compute layer runs Inference endpoints; the orchestration and logic layer uses S3 and Model monitoring; and the data and storage layer persists state via Model training. Shared IaC, security, and observability modules support all layers, keeping the platform automated, resilient, and easy to extend.
Set up cloud account, project, and infrastructure as code.
Provision core resources and networking for Inference endpoints.
Implement the workload logic using S3 and Model monitoring.
Add the data and storage layer via Model training.
Wire up automation, monitoring, and cost controls.
Test scaling, failures, and security, then refine.
Document the architecture, run a demo, and prepare the viva report.
Architect cloud-native, scalable systems
Apply MLOps on cloud and Cloud ML workflows
Provision infrastructure with automation
Secure, monitor, and optimize cloud workloads
Present and defend a complete cloud project in viva
Add multi-cloud and hybrid deployment
Introduce advanced AI and analytics on the platform
Add disaster recovery and global replication
Optimize cost with predictive autoscaling
The Serverless ML Inference Service delivers a complete, production-grade cloud platform — from automated infrastructure to secure, scalable services and observability. It is practical, cost-effective, and easy to explain, making it an excellent final year project that demonstrates advanced cloud computing skills.
