Reference 4 stops to get here

Blue-Green Deployment

Running two identical environments to enable zero-downtime model updates and rollbacks.

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. Blue-Green Deployment · you are here ✓ understood

Running two identical environments to enable zero-downtime model updates and rollbacks.

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

  • Deployment
  • MLOps
  • Release Strategy

Where it sits

Before this

Model Serving
Blue-Green 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. 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. Shipping AI Model Versioning Tracking different versions of models to enable reproducibility and rollback.