create_source
Create a new data source in BigML from a remote URL. The source is the first step in the ML pipeline — raw data is imported and parsed for further processing into datasets. Supports CSV files, JSON, Excel files, and other formats accessible via URL.
create_batch_prediction
Create a batch prediction to generate predictions for an entire dataset at once. More efficient than individual predictions when processing large volumes of data. The batch prediction runs asynchronously; check its status to know when results are ready.
update_resource
Update a BigML resource's mutable attributes. BigML resources are mostly immutable — only metadata fields like name, description, tags, and category can be updated after creation.
list_resources
List BigML resources of a given type with filtering, ordering, and pagination. Returns a paginated list of resources along with metadata about the total count and navigation. Use filters to narrow results by name, tags, creation date, and other resource-specific fields.
create_cluster
Create an unsupervised cluster model to group data instances by similarity. Computes centroids representing the center of each cluster. After creation, use predictions to assign new data points to the nearest cluster centroid.
manage_project
Create or update a BigML project. Projects organize resources into logical groups. Resources can be assigned to a project during creation, and all child resources inherit the project from their parent source.
create_dataset
Create a new dataset in BigML from a source, another dataset, or a list of datasets. Datasets are processed, structured representations of data with statistical summaries for each field. Supports sampling, filtering, field selection, and train/test splitting.
delete_resource
Permanently delete a BigML resource. This action is irreversible. Deleting a project will also delete all resources within that project.
execute_whizzml
Execute a WhizzML script on BigML's servers. WhizzML is a domain-specific language for automating ML workflows. Provide either an existing script ID or inline source code to execute. Executions run asynchronously — check the execution status and retrieve results when finished.
get_resource
Retrieve detailed information about a specific BigML resource by its ID. Returns the full resource object including status, fields, configuration, and results. Useful for checking the status of asynchronous operations (source creation, model training, evaluations, etc.) and retrieving model metrics.
train_model
Train a machine learning model from a dataset. Supports multiple model types: decision tree, ensemble (random forest, boosted trees), deepnet (neural network), logistic regression, linear regression, and time series. Choose the model type based on your task — classification, regression, forecasting, etc.
create_evaluation
Evaluate a supervised model's performance by comparing its predictions against a test dataset. Returns metrics like accuracy, precision, recall, and F-measure for classification tasks, or MSE and R-squared for regression tasks. Works with decision trees, ensembles, deepnets, logistic regressions, linear regressions, and time series models.
create_prediction
Generate a prediction from a trained supervised model. Supports predictions from decision trees, ensembles, deepnets, logistic regressions, linear regressions, and fusions. Provide input data as field-value pairs. Returns the predicted outcome along with confidence/probability information.
create_anomaly_detector
Create an anomaly detector using isolation forest algorithms. Identifies unusual data points in a dataset by measuring how easily they can be isolated from the rest of the data. After creation, use anomaly score predictions to evaluate how anomalous new data points are.
create_optiml
Create an OptiML for automated model selection and hyperparameter optimization. BigML will automatically create and evaluate hundreds of models with different algorithms and configurations to find the best performing model for your dataset. Supports classification and regression tasks.
Manage repositories, issues, and pull requests. Create and configure branches, star repositories, review code, and merge changes. Automate CI/CD workflows with GitHub Actions, manage workflow runs, secrets, and artifacts. Track issues with labels, milestones, and assignees. Search across code, repositories, issues, and users. Manage organizations, teams, and memberships. Create and manage projects, gists, packages, deployments, and environments. Access security alerts including code scanning, secret scanning, and Dependabot alerts. Read and write file contents in repositories. Manage webhooks, notifications, and codespaces.
Manage SharePoint sites, document libraries, lists, and files. Create, read, update, and delete lists and list items with custom columns. Upload, download, move, copy, and version files in document libraries. Search across sites, files, folders, lists, and list items using Microsoft Search. Manage permissions at site, list, and item levels with granular access control. Define and manage content types and site columns. Subscribe to webhooks for list and library change notifications. Retrieve site properties and search for sites across Microsoft 365.
Manage CRM data including Accounts, Contacts, Leads, Opportunities, Cases, and custom objects. Create, read, update, and delete records. Query data using SOQL and search across objects using SOSL. Perform bulk data operations for large-scale imports, exports, and migrations. Execute composite requests to batch multiple operations in a single API call. Access analytics, reports, and dashboards. Manage files and attachments associated with records. Interact with Chatter feeds, posts, and groups for social collaboration. Subscribe to real-time change events via Change Data Capture and Platform Events. Manage org metadata including custom objects, fields, layouts, and workflows. Query data using GraphQL for precise data retrieval across related objects.
Create, read, update, and delete records in Airtable bases and tables. Manage base schemas including creating tables and fields. Filter records using formulas, sort by fields, and scope queries to specific views. Upsert records to find, create, or update in a single call. Upload attachments to records, read and write record comments, list accessible bases, and receive real-time base change events through webhooks.
Manage Git repositories, pull requests, and CI/CD pipelines on Bitbucket Cloud. Create, fork, and configure repositories within workspaces and projects. Create, review, approve, merge, and decline pull requests with inline code comments. Browse source code, list commits, and manage branches and tags. Track issues with the built-in issue tracker. Trigger, monitor, and manage Bitbucket Pipelines. List workspace members, configure repository default reviewers and branch restrictions, create and manage repository webhooks, and search code across repositories.
Deploy, manage, and scale applications on Heroku's cloud platform. Create and configure apps, scale dynos, provision add-ons (databases, caching, etc.), manage configuration variables, build and release code, add custom domains and SSL certificates, manage collaborators and team permissions, configure pipelines for continuous delivery, set up log drains, and sync data with Salesforce via Heroku Connect. Subscribe to webhooks for real-time notifications on app changes, builds, releases, dyno lifecycle events, and more.
Build, manage, and deploy machine learning models via BigML's cloud platform. Create data sources from files, URLs, or external databases. Process datasets with transformations, sampling, filtering, and aggregation. Train supervised models (decision trees, ensembles, deepnets, logistic/linear regressions, time series) and unsupervised models (clusters, anomaly detectors, topic models, associations, PCA). Generate single or batch predictions including classifications, regressions, forecasts, anomaly scores, and centroids. Evaluate model performance with accuracy and precision metrics. Automate model selection with OptiML. Combine heterogeneous models using fusions. Script and automate ML workflows with WhizzML. Export models for local use. Manage organizations, projects, and external database connectors. Receive webhook notifications when resources are created or completed.
Common questions about connecting Bigml to AI agents with Metorial.
