Reference 4 stops to get here

Shadow Deployment

Running a new model alongside production without affecting user experience, for validation.

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. Shadow Deployment · you are here ✓ understood

Running a new model alongside production without affecting user experience, for validation.

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

  • Deployment
  • Testing
  • MLOps

Where it sits

Before this

Model Serving
Shadow Deployment

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Canary Deployment Gradually rolling out new model versions to subset of traffic before full deployment. Evaluation A/B Testing Comparing two model versions in production by routing traffic to each and measuring performance differences. Shipping AI Blue-Green Deployment Running two identical environments to enable zero-downtime model updates and rollbacks. Shipping AI Model Monitoring Tracking model performance, data distribution, and predictions in production to detect issues and degradation.