ceven

DataRobot

Triggers model retraining based on data drift, pulls predictive scores into your CRM, and monitors model health to alert your team when accuracy drops.

Try DataRobot in Ceven

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Why use Ceven?

  1. AI native DataRobot integration

    • Describe the outcome and Ceven picks the right DataRobot 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 DataRobot data, across all 836 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke DataRobot 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 DataRobot, when, and on whose behalf.
    • The agent pauses and asks when DataRobot is unclear instead of plowing ahead.

  4. Enterprise grade security

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

Supported tools

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

Get prediction
Pull a prediction score for a specific set of input features from a deployed model. Use this for real time decisioning.
Trigger retraining
Start a new model training run using a specified dataset. Use this when data drift is detected or new labels arrive.
List models
Pull a list of all available models, their current status, and their version numbers.
Get model metrics
Retrieve accuracy, precision, and recall scores for a specific model version to evaluate performance.
Update deployment
Promote a champion model to production or roll back to a previous version.
Search projects
Find machine learning projects by name or tag to locate the correct model ID.
Create dataset
Upload or link a new data source to be used for future training runs.
Get feature importance
Pull the list of features that most heavily influence model predictions for explainability.
Delete model
Remove an obsolete model version to clean up the workspace.
Get prediction history
Pull a log of recent predictions and the actual outcomes for a specific record.
Set alert threshold
Define the performance floor that triggers a notification when a model begins to drift.
Export model artifacts
Download the model binary or configuration for use in an external environment.

12 actions · scroll to see them all

Frequently asked questions

Ceven uses the DataRobot API token system to establish a secure connection. You provide your API key from the DataRobot user profile section, and Ceven stores this encrypted in our vault. Every request sent by an agent includes this token in the header to authenticate the session. We follow the principle of least privilege, so we recommend creating a dedicated service account in DataRobot with only the permissions needed for your specific workflows. This ensures that the agent cannot access sensitive projects or administrative settings that are outside the scope of the automated tasks you have configured.
Yes. Ceven can be configured to poll the DataRobot monitoring endpoints at a regular interval. When the model performance metrics, such as the area under the curve or mean absolute error, cross a threshold you define, Ceven triggers a workflow. This workflow can send a notification to your data science team, create a ticket in Jira, or even initiate an automated retraining job. By automating the monitoring loop, you ensure that your business decisions are always based on models that accurately reflect current real world data patterns without requiring a human to check a dashboard daily.
DataRobot imposes rate limits on its API to ensure platform stability, and these limits can vary based on your specific subscription tier. If a Ceven workflow attempts to pull thousands of individual predictions in a tight loop, you may encounter 429 Too Many Requests errors. To mitigate this, Ceven implements an exponential backoff strategy and encourages the use of batch prediction endpoints for large volumes of data. For users on lower tiers, we recommend scheduling heavy data pulls during off peak hours or using the batch processing tools to avoid hitting these limits during critical business operations.
Yes. Ceven can access the feature impact and prediction explanation endpoints in DataRobot. When a prediction is pulled, the agent can simultaneously request the local feature contributions. This means instead of just receiving a score, the agent can output a human readable explanation such as the loan was denied primarily because the debt to income ratio was too high. This is critical for industries like finance and healthcare where regulatory requirements demand that automated decisions be explainable and transparent to the end user or a compliance auditor.
No. Ceven acts as an orchestration layer, not a data warehouse. When you trigger a training run, Ceven tells DataRobot where the data is located or passes a reference to a dataset already stored within the DataRobot environment. The raw training data never resides on Ceven servers. We only process the metadata and the final prediction results required to execute your workflow. This architecture ensures that your sensitive training sets remain within your secure data perimeter and are handled according to the governance policies you have set up in DataRobot.
Ceven can manage the deployment lifecycle by calling the DataRobot deployment APIs. You can build a workflow that moves a model from a development project to a staging environment for testing, and finally to production after a human approves the results. The agent can automate the swapping of the champion and challenger models, ensuring there is zero downtime for your application. You can also set up a rollback workflow that automatically reverts to the previous version if the new model shows a spike in error rates within the first hour of deployment.
For very large datasets, the agent avoids individual API calls and instead utilizes the DataRobot batch prediction API. The workflow uploads the data to a temporary storage location, triggers the batch job, and then polls for completion. Once the job is finished, Ceven can trigger a downstream action, such as updating a database or sending a bulk email campaign based on the results. This method is significantly more efficient and avoids the rate limit issues associated with real time endpoints, making it the ideal choice for weekly or monthly scoring cycles.
That is the primary purpose of Ceven. We bridge the gap between the high level machine learning capabilities of DataRobot and the tools your business teams use every day. For example, you can create a workflow where a DataRobot prediction triggers a specific sequence in Salesforce, updates a row in Google Sheets, or sends a personalized message via Slack. This allows your operational staff to benefit from predictive AI without ever needing to log into the DataRobot interface or write a single line of Python code themselves.

Alternatives to DataRobot

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

H2O.ai logoH2O.aiDataiku logoDataikuRapidMiner logoRapidMinerAmazon SageMaker logoAmazon SageMaker

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

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