# BigML integrations: sources, datasets and models

> Create a source, create a dataset and create a model in BigML, and start a workflow when BigML sends “Resource creation finished”.

**Canonical page:** https://tray.ai/connectors/bigml-integrations/
**Categories:** AI
**Connects through:** HTTP Connector

## Overview

BigML is a machine learning platform for turning data into predictive models, from datasets and clusters to anomaly detection, forecasts and batch predictions. Tray.ai connects to BigML through the HTTP Connector, so your workflows can call the BigML API alongside the rest of your stack.

## Use cases

### Sources

Read, create, update, and delete sources in BigML from any Tray.ai workflow. The BigML API covers calls such as create a source and list sources.

- Create a source
- List sources
- Update a source

### Datasets

Read, create, and delete datasets in BigML from any Tray.ai workflow. The BigML API covers calls such as create a dataset and list datasets.

- Create a dataset
- List datasets
- Retrieve a dataset

### Models

Read, create, and delete models in BigML from any Tray.ai workflow. The BigML API covers calls such as create a model and list models.

- Create a model
- List models
- Retrieve a model

### Predictions

Read and create predictions in BigML from any Tray.ai workflow. The BigML API covers calls such as create a prediction and create a batch prediction.

- Create a prediction
- Create a batch prediction
- List predictions

### Projects

Read, create, update, and delete projects in BigML from any Tray.ai workflow. The BigML API covers calls such as create a project and list projects.

- Create a project
- List projects
- Update a project

### BigML events

Start a Tray.ai workflow the moment something happens in BigML. BigML sends events such as resource creation finished and resource creation failed to a Tray.ai webhook trigger.

- Resource creation finished
- Resource creation failed

## Challenges Tray.ai solves

### Keeping BigML credentials out of logs

A raw API call usually means pasting a key or token into the request, where it ends up in step inputs, logs, and screenshots.

**How Tray.ai helps:** Store BigML credentials once as a Tray.ai authentication and reference them from the HTTP Connector. Tray.ai masks authentication values in logs, so the secret never shows up in a step's input or output.

### Going from the BigML API docs to a working call

Translating an example request from API docs into a workflow step means copying the method, URL, headers, and body by hand.

**How Tray.ai helps:** Paste a cURL example from the BigML API docs into the HTTP Connector and Tray.ai fills in the method, URL, headers, and body for you.

### Handling files and non-JSON responses

Not every API answers in JSON. Some return XML, files, or binary data, or expect a multipart upload.

**How Tray.ai helps:** The HTTP Connector parses JSON and XML responses, returns files you can pass to the next step, and sends multipart form data for uploads.

## Frequently asked questions

### Does Tray.ai connect to BigML?

Yes. Tray.ai connects to BigML through the HTTP Connector, which calls the BigML API directly. Any call the BigML API documents can run as a step in a Tray.ai workflow.

### How does Tray.ai authenticate with BigML?

The BigML API uses an API key in the query string. You save it once as a Tray.ai authentication, and the HTTP Connector adds it to every request.

### Can I get a dedicated BigML connector?

BigML runs through the HTTP Connector rather than a dedicated connector. If your team wants one, the Tray.ai connector SDK can build a dedicated BigML connector from the BigML API.

## Learn more

- Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/
- Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/
- Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/
- Book a demo: https://tray.ai/contact/