Building a Multi-Cloud ML Deployment Platform ☁️

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.