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API Reference
Build powerful ML applications with our RESTful API. Comprehensive documentation, SDKs, and code examples.
Quick Start
Install the Python SDK
pip install onemlMake your first API call
import oneml
client = oneml.Client(api_key="your-api-key")
# Train a model
model = client.models.train(
dataset="your-dataset-id",
algorithm="random_forest",
target="price"
)
# Make predictions
predictions = client.models.predict(
model_id=model.id,
data={"features": [1.2, 3.4, 5.6]}
)API Endpoints
POST
/v1/models/trainModelsTrain a new machine learning model with your dataset
POST
/v1/models/predictInferenceRun inference on a trained model
GET
/v1/models/{model_id}ModelsRetrieve model details and metadata
POST
/v1/datasets/uploadDataUpload and preprocess training data
GET
/v1/jobs/{job_id}/statusJobsCheck training job status and progress
POST
/v1/embeddingsEmbeddingsGenerate vector embeddings for text or images
Official SDKs
API Features
Rate Limits
Generous rate limits with 10K requests/min for Pro plans and unlimited for Enterprise.
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