The Cloud Based Recommendation Engine is an advanced cloud computing project that combines Versioning and Model training, built with Docker. 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 training and Docker, 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 Versioning, orchestrated with Docker and Model training. 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.
Docker
SageMaker / Vertex AI
AWS / Azure / GCP
Docker / Kubernetes
Terraform / IaC
Serverless services
Monitoring and CI/CD
Lambda
MLflow
Cloud-native system with Versioning and Model training
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 Versioning; the orchestration and logic layer uses Docker and Model training; and the data and storage layer persists state via Inference endpoints. 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 Versioning.
Implement the workload logic using Docker and Model training.
Add the data and storage layer via Inference endpoints.
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 Model training and serving and Model versioning
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 Cloud Based Recommendation Engine 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.
