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Documentation Index

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nx1-deployer image v1.13.0
The May 2026 release for web portal users introduces new API endpoints spanning several features.

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New features

New features recently added to the NexusOne platform.

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AutoML

AutoML is a new NexusOne feature that makes building machine learning models much simpler for you. You can now train and deploy three types of ML models directly from NexusOne: Each model also comes with built-in SHapley Additive exPlanations, support, so you can always see a clear explanation of why the model made a particular decision. AutoML comes with several new sub-features with their individual API endpoints.

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AutoML feature engineering

You can now clean and shape your data for training using AutoML feature engineering. It prepares your data into a form the model can learn from. These endpoints let you create, approve, track, and retry that preparation before training starts. 
# Approve a proposal and start materialization
POST /api/automl/feature-engineering/jobs/{job_id}/approve

# Create a feature engineering proposal
POST /api/automl/feature-engineering

# Get a feature engineering proposal
GET /api/automl/feature-engineering/jobs/{job_id}

# Get materialization status
GET /api/automl/feature-engineering/jobs/{job_id}/materialization

# List feature engineering jobs
GET /api/automl/feature-engineering/jobs

# Re-run the crew on a failed job
POST /api/automl/feature-engineering/jobs/{job_id}/retry

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AutoML training

You can now submit and manage machine learning model training jobs using AutoML training. These endpoints let you list supported algorithms, submit, retrieve, or delete jobs, and manually trigger runs. 
# DAG status writeback with PSK auth
PUT /api/automl/train/jobs/{job_id}/status

# Delete a training job
DELETE /api/automl/train/jobs/{job_id}

# Get a training job
GET /api/automl/train/jobs/{job_id}

# List supported AutoML algorithms
GET /api/automl/algorithms

# List training jobs
GET /api/automl/train/jobs

# Manually trigger an AutoML training DAG
POST /api/automl/train/jobs/{job_id}/trigger

# Submit an AutoML training job
POST /api/automl/train

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Deployments

You can now deploy a trained machine learning model to production as a batch job or an online endpoint. Batch jobs score data on a schedule. Online endpoints respond to requests in real time. These endpoints let you create, list, promote, stop, and delete deployments. 
# Create a deployment for a model
POST /api/models/{model}/deployments

# Get a deployment
GET /api/models/{model}/deployments/{name}

# Get recent K8s events for an online deployment
GET /api/models/{model}/deployments/{name}/events

# List a model's deployments
GET /api/models/{model}/deployments

# Online deployment HTTP proxy with PSK bearer
GET     /api/models/{model}/deployments/{name}/run/{path}
PUT     /api/models/{model}/deployments/{name}/run/{path}
POST    /api/models/{model}/deployments/{name}/run/{path}
DELETE  /api/models/{model}/deployments/{name}/run/{path}
PATCH   /api/models/{model}/deployments/{name}/run/{path}
HEAD    /api/models/{model}/deployments/{name}/run/{path}
OPTIONS /api/models/{model}/deployments/{name}/run/{path}

# Promote a deployment to a new model version or alias
POST /api/models/{model}/deployments/{name}/promote

# Run callback: batch DAG to API with PSK auth
POST /api/models/{model}/deployments/{name}/runs

# Set the Keycloak roles allowed to invoke / manage a deployment
PUT /api/models/{model}/deployments/{name}/allowed_roles

# Soft-delete a deployment
DELETE /api/models/{model}/deployments/{name}

# Stop a deployment
POST /api/models/{model}/deployments/{name}/stop

# Trigger an immediate batch run
POST /api/models/{model}/deployments/{name}/trigger

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Catalog

You can now search across all DataHub entity types from a single endpoint. Entity types are the categories of assets that DataHub tracks, such as datasets, charts, and dashboards. 
# Unified search across DataHub entity types
GET /api/catalog/search

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DataHub proxy

You can now send requests directly to the DataHub General Metadata Service (GMS). GMS is the core backend that stores and manages all your metadata. These endpoints let you read, write, and manage any DataHub entity by forwarding requests directly to GMS. 
DELETE  /api/datahub/{path}
GET     /api/datahub/{path}
HEAD    /api/datahub/{path}
OPTIONS /api/datahub/{path}
PATCH   /api/datahub/{path}
POST    /api/datahub/{path}
PUT     /api/datahub/{path}

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Documents

You can now store, search, and enrich documents in NexusOne. Documents are files or records indexed and queryable across your NexusOne environment. CrewAI can enrich the documents with metadata and push suggestions to DataHub. Server-Sent Events (SSE) streaming then lets you track that progress in real time. These endpoints let you upload, retrieve, search, delete, and stream enrichment progress. 
# Confirm crew enrichment suggestions and push to DataHub
PATCH /api/documents/{document_id}/enrichment

# Delete a document
DELETE /api/documents/{document_id}

# Fetch full document text by DataHub URN
GET /api/documents/content

# Get a document by ID
GET /api/documents/{document_id}

# List the caller's documents
GET /api/documents

# Search DataHub documents
GET /api/documents/search

# Stream enrichment crew progress (SSE)
GET /api/documents/{document_id}/enrichment/stream

# Upload a document
POST /api/documents

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JupyterHub

JupyterHub now has two new environment variables, these include:
  • DATAHUB_API_URL: The the internal cluster address of the DataHub Generalized Metadata Service (GMS) backend. The JupyterHub DataHub panel extension uses it to query and write metadata programmatically.
  • DATAHUB_FRONTEND_URL: The public URL of the DataHub UI. When you click a link in the JupyterHub panel extension, it opens the corresponding dataset in the DataHub UI.

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MCP servers

You can now manage MCP server instances through the API. Model Context Protocol (MCP) is an open standard for connecting AI assistants to external tools and data sources. These endpoints let you create, read, update, and delete MCP server instances. 
# Create MCP server
POST /api/mcps

# Delete MCP server
DELETE /api/mcps/{server_id}

# Get MCP server
GET /api/mcps/{server_id}

# List MCP servers
GET /api/mcps

# Update MCP server
PUT /api/mcps/{server_id}

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MLflow

MLflow gives you read-only access to the MLflow Model Registry, either through the UI or via API.

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API

You can now browse and inspect registered machine learning models and their versions directly from the portal or via API endpoints. These endpoints let you list all registered models, fetch a specific model, and retrieve its full version history. 
# Get a registered MLflow model
GET /api/mlflow/registered-models/{name}

# List all versions for a registered model
GET /api/mlflow/registered-models/{name}/versions

# List registered MLflow models
GET /api/mlflow/registered-models

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Single Sign-On

The MLflow UI now uses the same Single Sign-On (SSO) login as the rest of the NexusOne platform. When you visit your MLflow URL, NexusOne redirects you to the Keycloak login page instead of a separate MLflow login screen. That means, if you are already logged in to NexusOne, you are automatically logged in to MLflow without having to enter your credentials again.

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Enhancements

Enhancements to existing app features on the NexusOne platform.

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App manager

The App manager feature now has three new endpoints. Two let you attach and retrieve a JupyterHub notebook in an app pipeline. One retrieves a specific secret’s value for an app. 
# Attach a notebook reference to an app version
PUT /api/app/versions/{version_id}/notebook

# Get a secret
GET /api/app/{app_id}/secrets/{secret_name}

# Get the notebook reference attached to an app version
GET /api/app/versions/{version_id}/notebook

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Upgrades

Version upgrades to existing apps on the NexusOne platform.

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Superset v6.1.0 upgrade

Superset now runs v6.1.0, upgraded from v4.1.2. This version includes new UI features, performance enhancements, and bug fixes for dashboards and data exploration.