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Technology Stack

Layer Technology Purpose
Front-End Angular Web application portal for browsing, configuring, and invoking AI services
Back-End Python Flask Powers each AI service HTTP endpoint; orchestration logic; Azure component integration
Data Storage PostgreSQL Primary relational database for metadata, execution logs, and application state
Container Orchestration Azure Kubernetes Service (AKS) All AI services run in Docker containers, ensuring portability and scalability
Container Registry Azure Container Registry (ACR) Stores private Docker images for all AI services
Artifact Storage Azure Blob Storage Public Artifactory for service ZIPs; model artifacts; pipeline outputs
Secret Management Azure Key Vault + HashiCorp Vault All connection strings, API keys, credentials stored and retrieved at runtime
ML Experiment Tracking MLFlow Automatic experiment logging, comparison, and visualization for model training
GPU Compute (Cloud) RunPod, Denvr, Azure GPU VMs On-demand GPU nodes for training and heavy inference
Distributed Processing Ray Parallel task execution for large-scale video/data processing
Logging ELK Stack (Elasticsearch) Structured execution logs indexed for search and monitoring
IDE Integration Agile Workbench (VS Code plugin) Upload service ZIPs to Public Artifactory directly from VS Code