> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nx1cloud.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Non-web portal users

> nx1-deployer `v1.13.0` release notes for non-web portal users, covering new AutoML, Documents, MCP server, and MLflow APIs, observability dashboards, security fixes, and platform component upgrades.

<Note>nx1-deployer image `v1.13.0`</Note>

The May 2026 release for non-web portal users introduces new API endpoints for AutoML, Documents, MCP servers, and MLflow,
adds pre-built Grafana dashboards and automated alerts, patches several CVEs, and upgrades key platform components.

## New features

New features recently added to the NexusOne platform.

### AutoML

AutoML is a new NexusOne feature that makes building machine learning models simpler.
You can now train and deploy three types of ML models directly from NexusOne:

* [LightGBM](https://lightgbm.readthedocs.io/en/stable/)
* [Vowpal Wabbit](https://vowpalwabbit.org/)
* [Isolation Forest](https://en.wikipedia.org/wiki/Isolation_forest)

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.

#### 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.

The following endpoints let you create, approve, track, and retry that preparation before training starts:

* [Approve a proposal and start materialization](/api-reference/endpoints/automl-feature-engineering/approve-a-proposal-and-start-materialization)
* [Create a feature engineering proposal](/api-reference/endpoints/automl-feature-engineering/create-a-feature-engineering-proposal)
* [Get a feature engineering proposal](/api-reference/endpoints/automl-feature-engineering/get-a-feature-engineering-proposal)
* [Get materialization status](/api-reference/endpoints/automl-feature-engineering/get-materialization-status)
* [List feature engineering jobs](/api-reference/endpoints/automl-feature-engineering/list-feature-engineering-jobs)
* [Re-run the crew on a failed job](/api-reference/endpoints/automl-feature-engineering/re-run-the-crew-on-a-failed-job)

#### AutoML training

You can now submit and manage machine learning model training jobs using AutoML training.

The following endpoints let you list supported algorithms, submit, retrieve, or delete jobs, and manually trigger runs:

* [DAG status writeback with PSK auth](/api-reference/endpoints/automl-training/dag-status-writeback)
* [Delete a training job](/api-reference/endpoints/automl-training/delete-a-training-job)
* [Get a training job](/api-reference/endpoints/automl-training/get-a-training-job)
* [List supported AutoML algorithms](/api-reference/endpoints/automl-training/list-supported-automl-algorithms)
* [List training jobs](/api-reference/endpoints/automl-training/list-training-jobs)
* [Manually trigger an AutoML training DAG](/api-reference/endpoints/automl-training/manually-trigger-an-automl-training-dag)
* [Submit an AutoML training job](/api-reference/endpoints/automl-training/submit-an-automl-training-job)

#### 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.

The following endpoints let you create, list, promote, stop, and delete deployments:

* [Create a deployment for a model](/api-reference/endpoints/deployments/create-a-deployment-for-a-model)
* [Get a deployment](/api-reference/endpoints/deployments/get-a-deployment)
* [Get recent K8s events for an online deployment](/api-reference/endpoints/deployments/recent-k8s-events-for-an-online-deployment)
* [List a model's deployments](/api-reference/endpoints/deployments/list-a-models-deployments)
* [Get a prediction from an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments)
* [Replace data in an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-1)
* [Send data to an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-2)
* [Remove data from an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-3)
* [Get options for an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-4)
* [Get header metadata from an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-5)
* [Update data in an online deployment](/api-reference/endpoints/deployments/http-proxy-for-online-deployments-6)
* [Promote a deployment to a new model version or alias](/api-reference/endpoints/deployments/promote-a-deployment-to-a-new-model-version-or-alias)
* [Run callback: batch DAG to API with PSK auth](/api-reference/endpoints/deployments/run-callback-from-the-batch-dag)
* [Set the Keycloak roles allowed to invoke or manage a deployment](/api-reference/endpoints/deployments/set-the-keycloak-roles-allowed-to-invoke-or-manage-this-deployment)
* [Soft-delete a deployment](/api-reference/endpoints/deployments/soft-delete-a-deployment)
* [Stop a deployment](/api-reference/endpoints/deployments/stop-a-deployment-and-tear-down-the-live-resource)
* [Trigger an immediate batch run](/api-reference/endpoints/deployments/trigger-an-immediate-run-for-batch-deployments)

### 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](/api-reference/endpoints/catalog/unified-search-across-datahub-entity-types)

### CrewAI

CrewAI comes with several new sub-features with their individual API endpoints.

#### Crew events

You can now poll Crew execution events using the following endpoint:

* [Poll crew execution events](/api-reference/endpoints/events/poll-crew-execution-events)

#### Crew runs

You can now view runs triggered by CrewAI using the following endpoints:

* [Get run](/api-reference/endpoints/crew-runs/get-run)
* [List run](/api-reference/endpoints/crew-runs/list-runs)

#### Crew templates

You can now manage Crew templates using the following endpoints:

* [Create template](/api-reference/endpoints/crew-templates/create-template)
* [Delete template](/api-reference/endpoints/crew-templates/delete-template)
* [Duplicate template](/api-reference/endpoints/crew-templates/duplicate-template)
* [Get template](/api-reference/endpoints/crew-templates/get-template)
* [List templates](/api-reference/endpoints/crew-templates/list-templates)
* [Update template](/api-reference/endpoints/crew-templates/update-template)

### 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.

The following endpoints let you read, write, and manage any DataHub entity by forwarding requests directly to GMS:

* [Get a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms)
* [Update a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-1)
* [Create a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-2)
* [Delete a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-3)
* [Get DataHub GMS entity options](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-4)
* [Get header metadata from a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-5)
* [Patch a DataHub GMS entity](/api-reference/endpoints/datahub-proxy/transparent-proxy-to-datahub-gms-6)

### 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.

The following let you upload, retrieve, search, delete, and stream enrichment progress:

* [Confirm crew enrichment suggestions and push to DataHub](/api-reference/endpoints/documents/confirm-crew-enrichment-suggestions-and-push-to-datahub)
* [Delete a document](/api-reference/endpoints/documents/delete-a-document)
* [Fetch full document text by DataHub URN](/api-reference/endpoints/documents/fetch-full-document-text-by-datahub-urn)
* [Get a document by ID](/api-reference/endpoints/documents/get-a-document-by-id)
* [List the caller's documents](/api-reference/endpoints/documents/list-documents-visible-to-you)
* [Search DataHub documents](/api-reference/endpoints/documents/search-datahub-documents)
* [Stream enrichment crew progress (SSE)](/api-reference/endpoints/documents/stream-enrichment-crew-progress)
* [Upload a document](/api-reference/endpoints/documents/upload-a-document)

### MCP servers

You can now manage Model Context Protocol (MCP) server instances through the API. MCP is an open standard
for connecting AI assistants to external tools and data sources.

The following endpoints let you create, read, update, and delete MCP server instances:

* [Create MCP server](/api-reference/endpoints/mcp-servers/create-mcp-server)
* [Delete MCP server](/api-reference/endpoints/mcp-servers/delete-mcp-server)
* [Get MCP server](/api-reference/endpoints/mcp-servers/get-mcp-server)
* [List MCP server](/api-reference/endpoints/mcp-servers/list-mcp-server)
* [Update MCP server](/api-reference/endpoints/mcp-servers/update-mcp-server)

### MLflow

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

#### API

You can now browse and inspect registered machine learning models and their versions directly using the MLflow API endpoints.

The following endpoints let you list all registered models, fetch a specific model, and retrieve its full version history:

* [Get a registered MLflow model](/api-reference/endpoints/mlflow/get-a-registered-mlflow-model)
* [List all versions for a registered model](/api-reference/endpoints/mlflow/list-all-versions-for-a-registered-model)
* [List registered MLflow models](/api-reference/endpoints/mlflow/list-registered-mlflow-models)

### Observability

Each deployed NexusOne tenant now has 19 pre-built Grafana dashboards covering every NexusOne platform component.
It also includes automated alerts that fire when components degrade or fail.

Dashboards are available for the following NexusOne components:

* AI API
* Airflow
* Apache Ranger
* Apache Spark
* DataHub
* Grafana
* Gravitino
* JupyterHub
* Keycloak
* Kyuubi
* Loki
* Metastore
* Mimir
* Namespace Overview
* PostgreSQL
* Superset
* Tempo
* Trino
* YuniKorn

NexusOne sends automated alerts for the following components, outlined in the table.

| Service    | Alert                                                  | Condition                     |
| ---------- | ------------------------------------------------------ | ----------------------------- |
| AI API     | 5xx error rate elevated                                | > 1 error/s                   |
| AI API     | Crew execution errors                                  | Any crew execution error      |
| Airflow    | DAG failures detected                                  | Any DAG in failed state       |
| Airflow    | Task failures detected                                 | Any task in failed state      |
| DataHub    | General Metadata Service (GMS) 5xx error rate elevated | > 1 error/s                   |
| JupyterHub | Server spawn duration high                             | p95 spawn time > 300 s        |
| Keycloak   | Failed login rate elevated                             | > 1 failed login/s            |
| Kyuubi     | Heap >85%                                              | Heap usage > 85%              |
| PostgreSQL | Connection saturation >90%                             | Connections > 90% of max      |
| Ranger     | Java virtual machine (JVM) memory >85% utilized        | JVM heap > 85%                |
| Spark      | Executor/driver pods failed                            | Any Spark pod in failed phase |
| Trino      | Query failure rate high                                | > 0.1 failures/min            |
| Trino      | Memory pool >85% utilized                              | Cluster memory > 85%          |
| YuniKorn   | Pending applications backlog                           | > 10 apps pending scheduling  |

Alerts fire after 5 minutes of a detected failure or degraded condition.

### S3 gateway

NexusOne now includes an S3 gateway that allows you to programmatically access object storage through any S3-compatible tool using
your existing NexusOne credentials instead of managing raw S3 keys. Keycloak authenticates every request, and Ranger
authorizes them. This gives platform admins full control over who can access what.

```bash theme={null}
# Use this as your S3 endpoint in any S3-compatible tool
https://s3-gateway-<env>.<domain>
```

## Bug fixes

Fixes to issues affecting apps or features on the NexusOne platform.

### OpenShift

Previously, platform components deployed on OpenShift clusters weren't reachable at their expected URLs. NexusOne created routes
without a host field, meaning components weren't reachable at their expected URLs.

NexusOne now assigns every component, such as JupyterHub, Trino, or Superset, a route with the correct hostname.

### Security patches

This release patches several CVEs across a few container images running in NexusOne.

| App                | CVE              | Resolution                                                                                |
| ------------------ | ---------------- | ----------------------------------------------------------------------------------------- |
| gravitino-rest     | CVE-2026-4878    | Switched base image from `eclipse-temurin:17-jre-jammy` to `eclipse-temurin:17-jre-noble` |
| keycloak           | CVE-2026-4878    | Updated to the fixed version of libcap and cleaned package manager caches                 |
| nginx-unprivileged | CVE-2026-4367    | Upgraded libxpm to the latest version                                                     |
| `oauth2-proxy`     | CVE-2026-34986   | Upgraded `oauth2-proxy` image from `v7.15.1` to `v7.15.2`                                 |
| Superset           | CVE-2024-12797   | Upgraded cryptography version                                                             |
| Superset           | CVE-2024-39689   | Upgraded certifi package                                                                  |
| Superset           | CVE-2024-52338   | Upgraded pyarrow                                                                          |
| Superset           | CVE-2026-4878    | Upgraded libcap2                                                                          |
| Trino              | PRISMA-2023-0067 | Upgraded com.fasterxml.jackson.core from `v2.14.2` to `v2.18.2`                           |

## Enhancements

Enhancements to existing app features on the NexusOne platform.

### 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](/api-reference/endpoints/app-manager/attach-a-notebook-reference-to-an-app-version)
* [Get a secret](/api-reference/endpoints/app-manager/get-a-secret)
* [Get the notebook reference attached to an app version](/api-reference/endpoints/app-manager/get-the-notebook-reference-attached-to-an-app-version)

### Apache Ranger

The Ranger platform component is now a shared service across all tenants. Previously each NexusOne tenant ran its own Ranger instance,
now it's deployed once and shared across all tenants.

Audit logs, the record of who accessed what data and when, were previously stored in Elasticsearch. They're now stored
in OpenSearch, which is NexusOne's standard search backend.

### Ollama

The Ollama platform component is now a shared service across all deployed NexusOne tenants. Previously, each tenant ran
its own Ollama instance, now it's deployed once and shared across all tenants. This reduces resource overhead without
any changes to the API or how you interact with it.

### Apache Spark

Spark jobs can now read from nested S3 directory structures automatically, without needing to explicitly list each
subdirectory. This is particularly useful for partitioned datasets stored in S3.

## Upgrades

Version upgrades to existing apps on the NexusOne platform.

### Apache Ranger `v2.8.0` upgrade

Apache Ranger now runs `v2.8.0`, upgraded from `v2.6.2`.

### Apache Spark and JupyterHub

Apache Iceberg now runs `v1.10.1`. If you are running Spark jobs from JupyterHub, you get the latest Iceberg features
and bug fixes for table format operations.

### 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.