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PUT
DAG status writeback

Authorizations

Authorization-PSK
string
header
required

Path Parameters

job_id
string<uuid>
required

Training job ID.

Body

application/json

Status writeback payload sent by the Airflow DAG.

status
enum<string>
required

Lifecycle of an AutoML training job.

  • READY: DAG uploaded but not yet triggered. The user opted out of auto-trigger at create time. Manual trigger flips it to QUEUED.
  • QUEUED: DAG triggered, waiting for Airflow to pick it up.
  • RUNNING: Airflow has the run going.
  • COMPLETE/FAILED: terminal.
Available options:
ready,
queued,
running,
complete,
failed
mlflow_run_id
string | null
registered_model_name
string | null
error_message
string | null

Response

Successful Response

A persisted training job.

job_id
string<uuid>
required
name
string
required
domain
string
required
owner
string
required
source_table
string
required
problem_type
enum<string>
required

Problem categories.

contextual_bandit is retired: kept only so persisted jobs still deserialize, rejected for new requests.

Available options:
binary_classification,
multiclass_classification,
regression,
ranking,
clustering,
anomaly_detection,
contextual_bandit
algorithm
enum<string>
required

AutoML algorithms.

The xgboost_*, sparkml_* and sklearn_* values are accepted for new training requests. The LightGBM, legacy isolation_forest and VW values are retired and kept only so persisted jobs still deserialize.

Available options:
lightgbm_classifier,
lightgbm_regressor,
lightgbm_ranker,
xgboost_classifier,
xgboost_regressor,
xgboost_ranker,
isolation_forest,
vw_classifier,
vw_regressor,
vw_contextual_bandit,
sparkml_logistic_regression,
sparkml_linear_regression,
sparkml_decision_tree_classifier,
sparkml_decision_tree_regressor,
sparkml_random_forest_classifier,
sparkml_random_forest_regressor,
sparkml_mlp_classifier,
sparkml_kmeans,
sklearn_isolation_forest
preset
enum<string>
required

Tuning presets shared across algorithms.

Available options:
fast,
balanced,
best_quality
status
enum<string>
required

Lifecycle of an AutoML training job.

  • READY: DAG uploaded but not yet triggered. The user opted out of auto-trigger at create time. Manual trigger flips it to QUEUED.
  • QUEUED: DAG triggered, waiting for Airflow to pick it up.
  • RUNNING: Airflow has the run going.
  • COMPLETE/FAILED: terminal.
Available options:
ready,
queued,
running,
complete,
failed
created_at
string<date-time>
required
updated_at
string<date-time>
required
rendered_dag
string | null

The rendered DAG source. Populated on creation.

mlflow_run_id
string | null
mlflow_experiment_name
string | null
registered_model_name
string | null
airflow_dag_id
string | null
airflow_dag_url
string | null
error_message
string | null
feature_engineering_job_id
string<uuid> | null