Standard 4 stops to get here · leads to 1

Model Endpoint

A deployed service exposing a model's predictions via API requests.

Your route here

4 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Inference ✓ understood

    Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.

  4. Model Serving ✓ understood

    Deploying trained models as services that can handle prediction requests in production environments.

  5. Model Endpoint · you are here ✓ understood

A deployed service exposing a model’s predictions via API requests.

This concept is essential for understanding practical deployment and forms a key part of modern AI systems.

  • Model Serving
  • API
  • Deployment

Where it sits

Before this

Model Serving
Model Endpoint

Leads to

Model Extraction

Explore nearby

Shipping AI REST API A web service interface commonly used for serving model predictions over HTTP. Shipping AI gRPC A high-performance RPC framework often used for low-latency model serving. Shipping AI Inference Latency The time delay between submitting input and receiving output from a deployed model, critical for real-time applications.