Building a Multi-Cloud ML Deployment Platform ☁️
Unified ML deployment across AWS, GCP, and Azure. Deep dive into App's architecture using Pulumi, Ansible, and real-time monitoring with Socket.IO.
Deploying ML models to production shouldn't require navigating three different cloud consoles. Here's how we built ML Dashboard.
The Problem
Data science teams waste hours fighting cloud-specific deployment documentation when they should focus on model development.
Our Solution
A unified control plane for deploying ML models across:
- AWS SageMaker
- Google Cloud Vertex AI
- Azure Machine Learning
Architecture Deep Dive
Asynchronous Job Processing
Used BullMQ + Redis for non-blocking infrastructure operations:
- Deploy operations run in background
- Real-time status updates via Socket.IO
- Automatic retry logic for failed deployments
Infrastructure as Code
Pulumi Automation API provisions cloud resources programmatically:
- EC2 instances with custom AMIs
- Security groups and networking
- Auto-scaling configurations
Configuration Management
Ansible playbooks handle:
- Dependency installation
- Model server setup (VLLM, TensorFlow Serving)
- Service monitoring and logging
Real-Time Monitoring
WebSocket connections stream:
- Deployment progress
- Instance logs
- Cost tracking
- Performance metrics
Multi-Tenancy & Security
Each user gets isolated:
- Dedicated cloud resources
- Encrypted credentials
- Rate-limited API access
- Audit logging
Results
Teams deploy models 10x faster while maintaining enterprise-grade security and observability.
Multi-cloud doesn't have to mean multi-complexity.
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