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

# Web app guide

## Docs

- [Web app guide](https://docs.umnai.com/ui-guides/overview.md): Connect data, train and deploy models, and explore explanations in the web interface.
- [Sign in and explore the dashboard](https://docs.umnai.com/ui-guides/setup/sign-in.md): Access the platform and find your data, models and deployments.
- [Data Lab](https://docs.umnai.com/ui-guides/data/overview.md): Understand how source files become datasets for model training.
- [Prepare data](https://docs.umnai.com/ui-guides/data/prepare-data.md): Shape source files so ingestion and onboarding can run cleanly.
- [Storage connectors](https://docs.umnai.com/ui-guides/setup/storage-connectors.md): Connect an Amazon S3 bucket for schema creation and ingestion.
- [Processing compute configurations](https://docs.umnai.com/ui-guides/setup/compute-configurations.md): Choose machines for ingestion and dataset onboarding.
- [Feature Transform Services](https://docs.umnai.com/ui-guides/deployment/feature-transform-services.md): Run the transformation service needed by feature-engineered models.
- [Data schemas](https://docs.umnai.com/ui-guides/data/schemas.md): Retrieve source fields and define their properties before ingestion.
- [Encoding](https://docs.umnai.com/ui-guides/data/encoding.md): Understand how schema fields are transformed for model training.
- [Ingestion](https://docs.umnai.com/ui-guides/data/ingestion-overview.md): Understand how source files become validated datasources.
- [Ingestion configuration](https://docs.umnai.com/ui-guides/data/ingestion-configuration.md): Associate a schema with observation metadata mappings.
- [Ingestion jobs](https://docs.umnai.com/ui-guides/data/ingestion-jobs.md): Import source files and check the resulting datasource.
- [Datasources](https://docs.umnai.com/ui-guides/data/datasources.md): Check imported data and trace its processing history.
- [Onboarding](https://docs.umnai.com/ui-guides/data/onboarding-overview.md): Understand how a datasource becomes a dataset for training.
- [Onboarding configuration](https://docs.umnai.com/ui-guides/data/onboarding-configuration.md): Define how a datasource is prepared for training.
- [Onboarding jobs](https://docs.umnai.com/ui-guides/data/onboarding-jobs.md): Run onboarding and check that the output dataset is available.
- [Inspect datasets](https://docs.umnai.com/ui-guides/data/datasets.md): Review dataset metadata, distributions, anomalies and lineage.
- [Model Lab](https://docs.umnai.com/ui-guides/models/overview.md): Understand training resources, model outputs and evaluation.
- [Workspaces](https://docs.umnai.com/ui-guides/models/workspaces.md): Create a workspace to organise model development.
- [Model constraints](https://docs.umnai.com/ui-guides/models/constraints.md): Inspect constraints and select saved resources for training.
- [Training](https://docs.umnai.com/ui-guides/models/training-overview.md): Understand how induction builds an explainable model from a dataset.
- [Training compute configurations](https://docs.umnai.com/ui-guides/models/training-compute.md): Choose reusable compute resources for training.
- [Training configuration](https://docs.umnai.com/ui-guides/models/training-configuration.md): Define reusable settings for how a model learns from data.
- [Training jobs](https://docs.umnai.com/ui-guides/models/training-jobs.md): Run training and verify the resulting model.
- [Trained models](https://docs.umnai.com/ui-guides/models/models.md): Inspect a training output before deploying it.
- [Evaluate models](https://docs.umnai.com/ui-guides/deployment/evaluation.md): Evaluate a running deployment and interpret its metrics and plots.
- [Serving](https://docs.umnai.com/ui-guides/deployment/overview.md): Understand how a trained model becomes available for predictions and explanations.
- [Deployment configuration](https://docs.umnai.com/ui-guides/deployment/configuration.md): Configure serverless or dedicated serving resources.
- [Deploy models](https://docs.umnai.com/ui-guides/deployment/deployed-models.md): Deploy a trained model and verify that it is running.
- [Explanation views](https://docs.umnai.com/ui-guides/explanations/overview.md): Choose a view for predictions, learned rules or generated interpretations.
- [Local explanations](https://docs.umnai.com/ui-guides/explanations/local-explanations.md): Query a record and inspect its prediction and explanation.
- [Feature and module attributions](https://docs.umnai.com/ui-guides/explanations/attributions.md): Inspect how features and modules contribute to a prediction.
- [Model structure and decision rules](https://docs.umnai.com/ui-guides/explanations/decision-rules.md): Browse modules, partitions, conditions and rule expressions.
- [Module dependencies](https://docs.umnai.com/ui-guides/explanations/module-dependencies.md): Inspect dependency plots and use the Module Explorer.
- [Dependency graph](https://docs.umnai.com/ui-guides/explanations/dependency-graph.md): Trace the displayed computation from inputs through modules to output.
- [XNN-assisted LLM explanations](https://docs.umnai.com/ui-guides/explanations/llm-explanations.md): Generate and review an interpretation of the built-in sample data.
- [Symbolic explanations](https://docs.umnai.com/ui-guides/explanations/symbolic-explanations.md): Generate and review a written interpretation of a module’s rules.
- [Use explanations](https://docs.umnai.com/ui-guides/explanations/using-explanations.md): Retain the context needed to interpret and reuse explanations.