★ 20 Ways to Generate Stock Investment Profits with AI - 14. Cloud Deployment for Trading Models

 

14. Cloud Deployment for Trading Models

Introduction
Deploying your AI strategies in the cloud ensures scalable, reliable operations with minimal maintenance.

Platform Options

• AWS: EC2 for servers, Lambda for serverless inference, S3 for data storage.

• GCP: AI Platform, Cloud Functions, Cloud Storage.

• Azure: Azure ML, Functions, Blob Storage.

Deployment Steps

1. Containerization: Package your Python environment, model code, and dependencies into a Docker image.

2. Data Integration: Use cloud storage buckets to stage raw and processed data.

3. Scheduling: Configure AWS EventBridge or GCP Scheduler to trigger daily inference.

4. Monitoring: Hook into CloudWatch or Stackdriver for logs, failures, and latency alerts.

Benefit
A cloud‐hosted momentum model scaled to process 500 tickers with zero downtime and immediate alerts on data feed failures.

Conclusion
Cloud deployment streamlines operations, letting you focus on model improvements rather than infrastructure headaches.