ceven

Bigml

Connects your raw data sources to predictive models and pushes machine learning insights directly into your operational workflows.

Try Bigml in Ceven

Ask Ceven anything
Standard

Why use Ceven?

  1. AI native Bigml integration

    • Describe the outcome and Ceven picks the right Bigml calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your Bigml data, across all 45 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Bigml access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in Bigml, when, and on whose behalf.
    • The agent pauses and asks when Bigml is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach Bigml.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every Bigml action to support review and sign off.

Supported tools

Every action Ceven's agents can run on Bigml, and when to use it.

Create external connector
Use this when you need to link a new external database or search index to BigML for data ingestion.
Create project
Use this to group related machine learning resources and datasets into a single project container.
Delete project
Permanently remove a project and all its associated resources after confirming they are no longer needed.
Get external connector
Pull the configuration and current state of a specific external connector by its ID.
Get project
Retrieve metadata and configuration details for a specific project to verify its setup.
List correlations
Pull a list of existing correlation resources to identify relationships between different data variables.
Create dataset
Upload raw data to BigML to prepare it for training or analysis within a project.
Train model
Initiate the training process for a predictive model using a specified dataset.
Make prediction
Send a single data point to a deployed model to get a real time prediction result.
List models
Pull all available models within a project to identify the most recent version for deployment.
Delete connector
Remove an external connector that is no longer providing data to your workflows.
Search resources
Query for specific BigML resources by name or tag to avoid creating duplicate models.

12 actions · scroll to see them all

Frequently asked questions

Ceven acts as a secure conduit between your data sources and BigML. We do not store your raw training data on our own servers; instead, we facilitate the movement of data from your connector to the BigML API. All API calls are encrypted using industry standard TLS. The permissions granted to Ceven are scoped specifically to the BigML project you select, ensuring the agent cannot access other unrelated projects in your organization. You can audit every request made by the agent in the Ceven logs to see exactly what data was sent to the BigML endpoint and when.
Yes. You can build a workflow that triggers the Train model action on a schedule or based on a data event. For example, you can set a trigger to run every Sunday night that pulls the latest data via an external connector and tells BigML to create a new model version. Ceven can then compare the accuracy of the new model against the old one and update the prediction endpoint automatically if the performance improves. This ensures your business logic always relies on the most current data patterns without manual data science work.
Ceven implements a robust retry logic with exponential backoff for BigML API calls. If a request fails due to a temporary network glitch, the agent will attempt the call again. If the failure is due to a permanent error, such as an invalid project ID or a schema mismatch in your dataset, Ceven will halt the workflow and send a notification to the owner with the exact error message from BigML. This prevents the system from making incorrect business decisions based on missing or failed predictions.
Ceven operates within the constraints of your specific BigML plan. A critical limitation to note is that certain advanced model types and higher concurrency limits are gated behind BigML Enterprise tiers. If your workflow attempts to create a resource that exceeds your plan limits, BigML will return a 403 Forbidden or 429 Too Many Requests error. Ceven will report this as a quota limit reached error. We recommend monitoring your BigML dashboard to ensure your plan supports the volume of predictions your automated workflows generate.
The agent uses the Create external connector action to establish a bridge between BigML and your data source. You provide the necessary credentials and endpoint details through the Ceven secure vault. Once the connector is established, Ceven can trigger BigML to pull data directly from that source. This means you do not have to manually upload CSV files every time you want to update a model. The agent manages the connection state and alerts you if the connector fails to authenticate during a scheduled data pull.
Absolutely. You can design a workflow that sends the same input data to multiple BigML models simultaneously. Ceven collects the predictions from each model and can then use a logic step to compare the results. For instance, you could use a majority vote system where the prediction used for the business action is the one agreed upon by two out of three models. This is a powerful way to validate model performance in a live environment before fully committing to a single predictive version.
Ceven supports all model types exposed through the BigML REST API, including decision trees, random forests, and neural networks. The agent interacts with these via the standard training and prediction endpoints. Because Ceven works at the API level, any new model type BigML releases that uses their standard API structure becomes immediately available for use in your workflows. You simply need to specify the model type in the training action or target the specific model ID in the prediction action.
For very large datasets, Ceven avoids pulling the data through its own layer to prevent bottlenecks. Instead, it instructs BigML to use external connectors to pull data directly from your cloud storage or database. This direct path ensures that the massive volume of data moves at the maximum speed supported by BigML and your data provider. Ceven only handles the control signals, such as starting the import process and verifying when the dataset is ready for the training phase to begin.

Alternatives to Bigml

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

DataRobot logoDataRobotH2O.ai logoH2O.aiRapidMiner logoRapidMiner

Try Ceven on your stack

Plug Ceven on top of the tools you already run. Connect Bigml and the rest of your stack, describe the outcome, and its agents handle the work end to end, days of it in minutes.

Get started for free