Reference 4 stops to get here

Canary Deployment

Gradually rolling out new model versions to subset of traffic before full deployment.

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. Canary Deployment · you are here ✓ understood

Gradually rolling out new model versions to subset of traffic before full deployment.

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

  • Deployment
  • A/B Testing
  • Release Strategy

Where it sits

Before this

Model Serving
Canary Deployment

Leads to

Nothing yet: a destination in its own right.

Explore nearby

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 Shadow Deployment Running a new model alongside production without affecting user experience, for validation. Shipping AI MLOps Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.