ML Inference Stack

A machine learning inference stack is a software architecture designed to deploy, serve, optimize, and scale trained machine learning models in production environments. These architectures enable applications to generate predictions, classifications, recommendations, or generated content efficiently while supporting reliable, low-latency operation. They are commonly used for AI assistants, recommendation systems, search ranking, computer vision, speech recognition, fraud detection, and other production AI applications.

The primary goal of a machine learning inference stack is to deliver model predictions efficiently, reliably, and at scale.

Typical Architecture

A common machine learning inference architecture looks like this:

User or Application
         ↓
Application Interface
         ↓
Inference API
         ↓
Model Serving
         ↓
Compute Infrastructure
         ↓
Monitoring and Analytics

Additional systems often support retrieval, orchestration, caching, and personalization.

Simple Architecture

A minimal machine learning inference stack may include:

Application
Inference API
Single Model Server
Basic Monitoring

This architecture supports many lightweight production AI applications.

Production Architecture

A larger production deployment may include:

Application Platform
Inference Gateway
Load Balancing
Distributed Inference Cluster
Streaming Responses
Model Routing
Autoscaling Infrastructure
Retrieval Pipelines
Semantic Caching
Monitoring Systems
Analytics Platforms
Safety Systems
Experimentation Framework
Feature Stores
Personalization Services