Registering and Versioning Scorers

Scorers can be registered to MLflow experiments for version control and team collaboration.

Supported Scorers

Scorer Type	Supported
Custom LLM Judges	✅
Code-based Scorers	❌
Guidelines Judges	❌ (Use MLflow Prompt Registry instead)
Built-in Judges	✅

UI

Registering a Scorer

When you create a judge using the Judge Builder UI, it is automatically registered to the current experiment as version 1.

Updating a Scorer

1. Navigate to the Judges tab in your experiment
2. Click the Edit button on the scorer you want to update
3. Modify the scorer configuration (instructions, model, output type, etc.)
4. Click Save. This will create a new version of the scorer.

Deleting a Scorer

1. Navigate to the Judges tab in your experiment
2. Click the Delete button on the scorer you want to remove

SDK

Prerequisite

Judges are registered to an MLflow Experiment (not Run-level).

python

import mlflow

mlflow.set_tracking_uri("your-tracking-uri")
mlflow.create_experiment("evaluation-judges")

Define a sample template-based LLM scorer:

python

from mlflow.genai.judges import make_judge

quality_judge = make_judge(
    name="response_quality",
    instructions=("Evaluate if {{ outputs }} is high quality for {{ inputs }}."),
    model="anthropic:/claude-opus-4-1-20250805",
    feedback_value_type=str,
)

Registering a Scorer

To register a judge to the experiment, call the register method on the judge instance.

python

# Register the judge
registered = quality_judge.register()
# You can pass experiment_id to register the judge to a specific experiment
# registered = quality_judge.register(experiment_id=experiment_id)

Updating a Scorer

Registering a new scorer with the same name will create a new version.

python

# Update and register a new version of the judge
quality_judge_v2 = make_judge(
    name="response_quality",  # Same name
    instructions=(
        "Evaluate if {{ outputs }} is high quality, accurate, and complete "
        "for the question in {{ inputs }}."
    ),
    model="anthropic:/claude-3.5-sonnet-20241022",  # Updated model
    feedback_value_type=str,
)

# Register the updated judge
registered_v2 = quality_judge_v2.register(experiment_id=experiment_id)

Loading a Scorer

To load a registered scorer, use the get_scorer function.

python

from mlflow.genai import get_scorer

# Get the latest version
latest_judge = get_scorer(name="response_quality")
# or specify experiment_id to get a scorer from a specific experiment
# latest_judge = get_scorer(name="response_quality", experiment_id=experiment_id)

Listing Scorers

The list_scorers function returns a list of the scorers registered in the experiment.

python

from mlflow.genai import list_scorers

all_scorers = list_scorers(experiment_id=experiment_id)
for scorer in all_scorers:
    print(f"Scorer: {scorer.name}, Model: {scorer.model}")