Model Serving · Concept · 360° | Nexonoma

ConceptStructure#Machine Learning#Platform#Observability

Model Serving

Model serving describes the systems and infrastructure that expose trained machine learning models to production traffic, handling scaling, versioning, routing and observability. It includes serving APIs, model lifecycle management and resource orchestration. The goal is reliable, low‑latency inference and reproducible deployment pipelines.

Use this profile to understand the building block briefly, place it in the model, and switch to the 360° assessment when needed.

Content type

Concept

Theoretical construct: explains a term, principle, or mental model.

Classification level

Structure

What organizes, connects, or makes decisions possible.

Available perspectives

Open this building block directly in an already curated view.

Why is this building block relevant?

Concepts and practices for exposing trained machine learning models to production traffic, focusing on scalability, versioning and observability.

Position in the model

Where this building block is located in the topic model.

Technology & Infrastructure

AI & Machine Learning

Deployment & Inference

#AI#Machine Learning

Knowledge network

Explore how this building block connects to concepts, methods, technologies, and tools.

External references

These sources establish the term and its professional meaning.

Inference (Machine Learning) — Wikipedia (EN)TensorFlow Serving — Official documentation tensorflow/serving — GitHub repository

Connections as a list

All direct connections of the current building block in a compact text view.

Dependency · Implements

(1)

Triton Inference Server

Process · Enables

(1)

Self-Hosted Models

Additional classification

This classification shows where the building block typically matters, how demanding it is, and what kind of impact it has in the model.

Organizational level

Enterprise

The level within the organization (enterprise, domain, team) at which the AssetBlock is applied.