Standard 4 stops to get here

A/B Testing

Comparing two model versions in production by routing traffic to each and measuring performance differences.

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. A/B Testing · you are here ✓ understood

Where it sits

Before this

Model Serving
A/B Testing

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI Shadow Deployment Running a new model alongside production without affecting user experience, for validation. Shipping AI Canary Deployment Gradually rolling out new model versions to subset of traffic before full deployment. Shipping AI Model Monitoring Tracking model performance, data distribution, and predictions in production to detect issues and degradation. Shipping AI Experiment Tracking Recording hyperparameters, metrics, and artifacts from training runs for comparison and reproducibility. Evaluation Baseline Model A simple reference model (random, majority class, simple heuristic) used to benchmark more complex models against.