> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.umnai.com/guides/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.umnai.com/_mcp/server.

# REST API guide

## Docs

- [REST API guide](https://docs.umnai.com/guides/overview.md): Practical workflow guidance for building with the platform.
- [Quickstart](https://docs.umnai.com/guides/quickstart.md): Ingest sample data, train and deploy a model, then request your first prediction and explanation.
- [Authentication](https://docs.umnai.com/guides/setup/authentication.md): Choose how your integration authenticates to the platform.
- [Account storage](https://docs.umnai.com/guides/setup/account-storage.md): Choose whether Umnai or your organisation provides the storage used for platform-created resources.
- [Account credentials](https://docs.umnai.com/guides/setup/account-credentials.md): Authenticate account-scoped integrations and automation workloads.
- [User credentials](https://docs.umnai.com/guides/setup/user-credentials.md): Authenticate API actions that need to run as a specific user.
- [Compute](https://docs.umnai.com/guides/setup/compute.md): Create reusable compute configurations for processing and training jobs.
- [Data](https://docs.umnai.com/guides/data/overview.md): Prepare source data and turn it into model-ready datasets.
- [Prepare data](https://docs.umnai.com/guides/data/prepare-data.md): Shape source files so ingestion and onboarding can run cleanly.
- [Source storage](https://docs.umnai.com/guides/data/source-storage.md): Connect a read-only Amazon S3 source bucket for data ingestion.
- [Schema](https://docs.umnai.com/guides/data/schema/overview.md): Describe how Umnai should interpret source data during ingestion and onboarding.
- [Encoding](https://docs.umnai.com/guides/data/schema/encoding.md): Understand how schema fields are transformed for model training.
- [Ingestion](https://docs.umnai.com/guides/data/ingestion/overview.md): Turn source data into validated datasources.
- [Configuration](https://docs.umnai.com/guides/data/ingestion/configuration.md): Create reusable ingestion settings for datasource jobs.
- [Jobs](https://docs.umnai.com/guides/data/ingestion/jobs.md): Run ingestion to create or update datasources.
- [Datasources](https://docs.umnai.com/guides/data/ingestion/datasources.md): Inspect and manage the output of ingestion.
- [Onboarding](https://docs.umnai.com/guides/data/onboarding/overview.md): Turn datasources into datasets for training.
- [Configuration](https://docs.umnai.com/guides/data/onboarding/configuration.md): Define how datasources are prepared for training.
- [Jobs](https://docs.umnai.com/guides/data/onboarding/jobs.md): Run onboarding to create or update datasets.
- [Datasets](https://docs.umnai.com/guides/data/onboarding/datasets.md): Inspect and manage the output of onboarding.
- [Overview](https://docs.umnai.com/guides/models/overview.md): Train datasets into explainable models.
- [Workspaces](https://docs.umnai.com/guides/models/workspaces.md): Organise training jobs and models.
- [Configuration](https://docs.umnai.com/guides/models/training/configuration.md): Define how models are trained.
- [Constraints](https://docs.umnai.com/guides/models/training/constraints.md): Guide which modules are included during training.
- [Jobs](https://docs.umnai.com/guides/models/training/jobs.md): Run training to create models.
- [Models](https://docs.umnai.com/guides/models/training/models.md): Inspect and manage the output of training.
- [Overview](https://docs.umnai.com/guides/deployment/overview.md): Make trained models available for inference.
- [Configuration](https://docs.umnai.com/guides/deployment/configuration.md): Define how models are served.
- [Deployed models](https://docs.umnai.com/guides/deployment/deployed-models.md): Manage models that are available for inference.
- [Overview](https://docs.umnai.com/guides/views/overview.md): Generate views to inspect data, models, predictions, and explanations.
- [Response structure](https://docs.umnai.com/guides/views/response-structure.md): Understand the common shape of view responses.
- [Data views](https://docs.umnai.com/guides/views/data-views/overview.md): Inspect lineage and distribution for datasources and datasets.
- [Datasource lineage](https://docs.umnai.com/guides/views/data-views/datasource-lineage.md): Trace how a datasource was created from raw inputs.
- [Dataset lineage](https://docs.umnai.com/guides/views/data-views/dataset-lineage.md): Trace how a dataset was created from its datasource and raw inputs.
- [Dataset histogram](https://docs.umnai.com/guides/views/data-views/dataset-histogram.md): Inspect feature and target distributions in an onboarded dataset.
- [Model views](https://docs.umnai.com/guides/views/model-views/overview.md): Explore trained model structure and comparisons.
- [Structure](https://docs.umnai.com/guides/views/model-views/structure.md): Inspect modules, partitions, rules, bins, and model metadata.
- [Comparison](https://docs.umnai.com/guides/views/model-views/comparison.md): Compare the structure of two trained models.
- [Deployed model views](https://docs.umnai.com/guides/views/deployed-model-views/overview.md): Query deployed models and inspect prediction-level outputs.
- [Results](https://docs.umnai.com/guides/views/deployed-model-views/results.md): Retrieve prediction outputs and the raw explanation data behind them.
- [Feature attribution](https://docs.umnai.com/guides/views/deployed-model-views/feature-attribution.md): Understand how input features contributed to a prediction.
- [Module attribution](https://docs.umnai.com/guides/views/deployed-model-views/module-attribution.md): Understand how model modules and activated rules contributed to a prediction.
- [Decision actions](https://docs.umnai.com/guides/views/deployed-model-views/decision-actions.md): Inspect partition-level activations, attributions, and rule expressions.
- [Module dependency](https://docs.umnai.com/guides/views/deployed-model-views/module-dependency.md): Inspect how selected module features affect attribution across sampled values.
- [Anomalies](https://docs.umnai.com/guides/views/deployed-model-views/anomalies.md): Identify anomalous feature bins in deployed model data.
- [Strengths and weaknesses](https://docs.umnai.com/guides/views/deployed-model-views/strengths-and-weaknesses.md): Estimate prediction strength, uncertainty, and adjusted error.
- [Classification evaluation](https://docs.umnai.com/guides/views/deployed-model-views/classification-evaluation.md): Evaluate binary classification performance across thresholds.
- [Regression evaluation](https://docs.umnai.com/guides/views/deployed-model-views/regression-evaluation.md): Evaluate regression performance with error and fit metrics.
- [Administration](https://docs.umnai.com/guides/administration/overview.md): Manage account access, users, roles, and user-level settings.
- [Access control](https://docs.umnai.com/guides/administration/access-control.md): Choose the right identity for account-scoped and user-scoped workflows.
- [Organisations](https://docs.umnai.com/guides/administration/organisations.md): Retrieve the organisations available to your credentials.
- [Accounts](https://docs.umnai.com/guides/administration/accounts.md): Retrieve the accounts available to your credentials.
- [Users](https://docs.umnai.com/guides/administration/users.md): Retrieve users in the active account and invite new users.
- [Roles](https://docs.umnai.com/guides/administration/roles.md): Retrieve roles available for user assignment.
- [Profile](https://docs.umnai.com/guides/administration/profile.md): Retrieve the current user profile and switch the active account.
- [Settings](https://docs.umnai.com/guides/administration/settings.md): Retrieve and manage user-level settings.