Deploy ML Model Serving

See Extended Examples for complete configuration files and templates.

Deploy machine learning models to production with scalable serving infrastructure, monitoring, and A/B testing.

When to Use

• Deploying trained models to production for real-time inference
• Setting up REST or gRPC APIs for model predictions
• Implementing autoscaling for variable load patterns
• Running A/B tests between model versions
• Migrating from batch to real-time inference
• Building low-latency prediction services
• Managing multiple model versions in production

Inputs