AutoML

Create, manage, and monitor automated machine learning jobs. AutoML automatically selects the best model architecture, hyperparameters, and feature engineering for your dataset and prediction task.

All endpoints require authentication via X-API-Key header and the appropriate scope.

---

AutoMLJob Object

{
  "id": "aml_abc123",
  "name": "Churn Prediction Model",
  "status": "running",
  "description": "Predicting customer churn from usage and billing data",
  "dataset": "ds_customers_001",
  "problemType": "binary_classification",
  "predictorType": "tabular",
  "owner": "user_456",
  "organizationId": "org_xyz",
  "targetColumn": "churned",
  "preset": "best_quality",
  "timeLimit": 3600,
  "startedAt": "2025-02-01T10:00:00Z",
  "finishedAt": null,
  "error": null,
  "createdAt": "2025-02-01T09:55:00Z",
  "updatedAt": "2025-02-01T12:30:00Z"
}

---

GET /api/v1/automl/jobs

List all AutoML jobs accessible to the authenticated user.

Scope: ml-workbench:read

Query Parameters

Parameter	Type	Required	Description
status	string	No	Filter by status: queued, running, completed, failed, stopped
search	string	No	Search by job name or description
limit	integer	No	Number of results to return (default: 20)
offset	integer	No	Number of results to skip (default: 0)
sort	string	No	Sort field and direction, e.g. createdAt:desc

Response 200 OK

{
  "count": 10,
  "limit": 20,
  "offset": 0,
  "jobs": [
    {
      "id": "aml_abc123",
      "name": "Churn Prediction Model",
      "status": "running",
      "description": "Predicting customer churn from usage and billing data",
      "dataset": "ds_customers_001",
      "problemType": "binary_classification",
      "predictorType": "tabular",
      "owner": "user_456",
      "organizationId": "org_xyz",
      "targetColumn": "churned",
      "preset": "best_quality",
      "startedAt": "2025-02-01T10:00:00Z",
      "finishedAt": null,
      "createdAt": "2025-02-01T09:55:00Z",
      "updatedAt": "2025-02-01T12:30:00Z"
    }
  ]
}

---

POST /api/v1/automl/jobs

Create a new AutoML job.

Scope: ml-workbench:write

Datasets are S3-backed project volumes (uploaded through the UI or attached to a project). Reference one by its volume id (from GET /automl/datasets); a volume is a directory, so when it holds more than one data file, pass datasetFile with the file key (discover files with GET /automl/datasets/:id/files). You may also pass an s3:// path or a URL directly as dataset.

Request Body

{
  "name": "Churn Prediction Model",
  "dataset": "vol_customers_001",
  "datasetFile": "data/customers.csv",
  "targetColumn": "churned",
  "featureColumns": ["usage_days", "monthly_spend", "support_tickets", "plan_type"],
  "problemType": "tabular",
  "predictorType": "auto",
  "preset": "medium_quality",
  "timeLimit": 600,
  "metric": "accuracy",
  "hardware": { "cpu_count": 4, "memory_gb": 16, "disk_gb": 20, "gpu_count": 0 },
  "environmentId": "custom",
  "advanced": { "excludeModels": ["KNN"] }
}

Field	Type	Required	Description
name	string	Yes	Human-readable job name
dataset	string	Yes	Project dataset volume id (from GET /automl/datasets), an s3:// path, or a URL
targetColumn	string	Yes	Name of the column to predict
datasetFile	string	No	File key within the volume, when it holds more than one data file
featureColumns	string[]	No	Specific columns to use as features. All non-target columns used if omitted
problemType	string	No	Data modality: tabular (default), multimodal, timeseries
predictorType	string	No	auto (default, AutoGluon infers), BinaryClassifier, MultiClassifier, Regressor
preset	string	No	AutoGluon quality preset: best_quality, high_quality, medium_quality (default: medium_quality)
timeLimit	integer	No	Maximum training time in seconds (default: 600)
metric	string	No	Optimization metric (e.g. accuracy, f1, rmse). Default: accuracy
hardware	object	Yes	Pod sizing: { cpu_count, memory_gb, disk_gb, gpu_count } (gpu_count optional)
environmentId	string	No	Training environment: custom (default) or an existing environment id
advanced	object	No	Advanced AutoGluon options

Response 201 Created

{
  "id": "aml_abc123",
  "name": "Churn Prediction Model",
  "status": "queued",
  "createdAt": "2025-02-01T09:55:00Z"
}

---

GET /api/v1/automl/jobs/:id

Get a single AutoML job by ID.

Scope: ml-workbench:read

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	AutoML job ID

Response 200 OK

Returns the full AutoMLJob object.

---

DELETE /api/v1/automl/jobs/:id

Delete an AutoML job. Running jobs must be stopped before deletion.

Scope: ml-workbench:write

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	AutoML job ID

Response 204 No Content

---

POST /api/v1/automl/jobs/:id/stop

Stop a running AutoML job. The job will be marked as stopped and training will be terminated.

Scope: ml-workbench:write

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	AutoML job ID

Response 200 OK

{
  "id": "aml_abc123",
  "status": "stopped",
  "message": "AutoML job stopped successfully"
}

---

POST /api/v1/automl/jobs/:id/deploy

Deploy the best model from a completed AutoML job. Makes the model available for inference via the model registry.

Scope: ml-workbench:write

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	AutoML job ID

Response 200 OK

{
  "id": "aml_abc123",
  "modelId": "model_xyz789",
  "modelName": "Churn Prediction Model - Best",
  "status": "deployed",
  "message": "Best model deployed successfully"
}

---

GET /api/v1/automl/jobs/:id/logs

Retrieve training logs for an AutoML job.

Scope: ml-workbench:read

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	AutoML job ID

Query Parameters

Parameter	Type	Required	Description
lines	integer	No	Number of log lines to return (default: 100)
since	string	No	Return logs after this ISO 8601 timestamp

Response 200 OK

{
  "jobId": "aml_abc123",
  "logs": [
    {
      "timestamp": "2025-02-01T10:00:05Z",
      "level": "info",
      "message": "AutoML job started - problem type: binary_classification"
    },
    {
      "timestamp": "2025-02-01T10:02:15Z",
      "level": "info",
      "message": "Feature engineering completed - 12 features generated"
    },
    {
      "timestamp": "2025-02-01T10:15:30Z",
      "level": "info",
      "message": "Model training: LightGBM completed - f1: 0.891"
    }
  ]
}

---

GET /api/v1/automl/stats

Get aggregate statistics for AutoML jobs in the organization.

Scope: ml-workbench:read

Response 200 OK

{
  "total": 10,
  "running": 1,
  "completed": 6,
  "failed": 2,
  "queued": 1
}

---

GET /api/v1/automl/datasets

List the project dataset volumes (S3-backed) the authenticated user can train on. A volume is a directory; use GET /automl/datasets/:id/files to list the files inside it.

Scope: ml-workbench:read

Response 200 OK

{
  "datasets": [
    {
      "_id": "vol_customers_001",
      "name": "Customer Data Q4 2024",
      "s3Location": "s3://strongly/projects/proj_1/volumes/vol_customers_001",
      "sizeGB": 0.045,
      "format": "csv",
      "dataType": "structured",
      "projectId": "proj_1",
      "projectName": "Churn Analysis"
    }
  ],
  "projects": [
    { "_id": "proj_1", "name": "Churn Analysis", "isOwner": true }
  ]
}

---

POST /api/v1/automl/datasets/upload-url

Get a presigned URL to upload a local training dataset file. PUT the file bytes to the returned uploadUrl, then reference the returned s3Path as the dataset in POST /automl/jobs. This is the programmatic equivalent of the UI's dataset uploader.

Scope: ml-workbench:write

Request Body

Field	Type	Required	Description
filename	string	Yes	Dataset file name (e.g. titanic.csv)
contentType	string	No	MIME type (default text/csv)

Response 201 Created

{
  "datasetId": "f22a9764e2ed836b9799a50b",
  "uploadUrl": "https://s3.amazonaws.com/strongly-dev/mlops/automl/jobs/.../input/titanic.csv?X-Amz-...",
  "s3Path": "mlops/automl/jobs/f22a9764e2ed836b9799a50b/input/titanic.csv",
  "bucket": "strongly-dev"
}

Then PUT the raw file bytes to uploadUrl and create the job:

curl -X PUT "$uploadUrl" -H "Content-Type: text/csv" --data-binary @titanic.csv
# then POST /automl/jobs with { "dataset": "<s3Path>", "targetColumn": "Survived", ... }

---

GET /api/v1/automl/datasets/:id/files

List the data files inside a project dataset volume, so you can pick which one to train on (pass its key as datasetFile to POST /automl/jobs).

Scope: ml-workbench:read

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	Dataset volume id (from GET /automl/datasets)

Response 200 OK

{
  "volumeId": "vol_customers_001",
  "s3Location": "s3://strongly/projects/proj_1/volumes/vol_customers_001",
  "files": [
    { "key": "projects/proj_1/volumes/vol_customers_001/customers.csv", "name": "customers.csv", "size": 45000, "isDirectory": false }
  ]
}

---

GET /api/v1/automl/datasets/:id/columns

Get the column headers of a dataset file in a project volume (to choose the target and feature columns). Reads only the header row.

Scope: ml-workbench:read

Path Parameters

Parameter	Type	Required	Description
id	string	Yes	Dataset volume id

Query Parameters

Parameter	Type	Required	Description
file	string	No	File key within the volume. Optional when the volume has exactly one data file

Response 200 OK

{
  "volumeId": "vol_customers_001",
  "file": "projects/proj_1/volumes/vol_customers_001/customers.csv",
  "columns": ["usage_days", "monthly_spend", "support_tickets", "plan_type", "churned"]
}