ml-training

name	ml-training
description	Use when training prediction models, extracting metrics, configuring algorithms, or deploying models

ML Model Training Patterns

Reference patterns for training and deploying ML models via the Dataiku Python API.

Quick Start: Create and Train a Prediction Model

# Create ML task
ml_task = project.create_prediction_ml_task(
    input_dataset="MY_DATASET",
    target_variable="target_column",
    prediction_type="BINARY_CLASSIFICATION",  # or REGRESSION, MULTICLASS
    ml_backend_type="PY_MEMORY",
    guess_policy="DEFAULT",
    wait_guess_complete=True
)

# Train models (waits for completion)
trained_ids = ml_task.train(session_name="My Training Session")

# Get metrics for each trained model
for model_id in trained_ids:
    details = ml_task.get_trained_model_details(model_id)
    metrics = details.get_performance_metrics()
    algo = details.get_modeling_settings()["algorithm"]
    print(f"Model: {algo}")
    print(f"  AUC: {metrics.get('auc')}")
    print(f"  Log Loss: {metrics.get('logLoss')}")

Deploy Model to Flow

Important: Only deploy models when explicitly requested by the user. After training, show results and let the user decide. When deploying, confirm whether to create new or update existing.

First-time deployment (creates new saved model)

result = ml_task.deploy_to_flow(
    model_id=best_model_id,
    model_name="my_model",
    train_dataset="MY_DATASET"
)
# Returns: {"savedModelId": "...", "trainRecipeName": "..."}

Update existing saved model (new version)

saved_model = project.get_saved_model("my_model")
sm_id = saved_model.get_id()
ml_task.redeploy_to_flow(model_id=new_model_id, saved_model_id=sm_id)

Access Existing ML Task

analyses = project.list_analyses()
analysis = project.get_analysis(analyses[0]['analysisId'])
ml_tasks_info = analysis.list_ml_tasks()
ml_task = analysis.get_ml_task(ml_tasks_info['mlTasks'][0]['mlTaskId'])
model_ids = ml_task.get_trained_models_ids()

References

• Model metrics, get_performance_metrics(), common metrics table — see references/model-metrics.md
• Algorithm names (UPPERCASE), enable/disable, hyperparameters — see references/algorithm-config.md
• Feature importance, rawImportance, dummy filtering — see references/feature-importance.md