Remove other references to the cloud deployment guide and helm charts

Update MLflow Triton Plugin description and refer to upstream repo

README.md

@@ -34,8 +34,4 @@ NVIDIA Morpheus is an open AI application framework that provides cybersecurity
 ### Modifying Morpheus
 * [Contributing to Morpheus](./docs/source/developer_guide/contributing.md) - Covers building from source, making changes and contributing to Morpheus
 
-### Deploying Morpheus
-* [Morpheus Cloud Deployment Guide](./docs/source/cloud_deployment_guide.md) - Kubernetes and cloud based deployments
-
-
 Full documentation for the latest official release is available at [https://docs.nvidia.com/morpheus/](https://docs.nvidia.com/morpheus/).

docs/source/developer_guide/guides/5_digital_fingerprinting.md

@@ -56,7 +56,6 @@ Key Features:
  * Uses a model store to allow the training and inference pipelines to communicate
  * Organized into a docker-compose deployment for easy startup
  * Contains a Jupyter notebook service to ease development and debugging
- * Can be deployed to Kubernetes using provided Helm charts
  * Uses many customized stages to maximize performance.
 
 This example is described in `examples/digital_fingerprinting/production/README.md` as well as the rest of this document.
@@ -121,9 +120,7 @@ DFP in Morpheus is accomplished via two independent pipelines: training and infe
 ## Runtime Environment Setup
 ![Runtime Environment Setup](img/dfp_runtime_env.png)
 
-DFP in Morpheus is built as an application of containerized services​ and can be run in two ways:
-1. Using docker-compose for testing and development​
-1. Using helm charts for production Kubernetes deployment​
+DFP in Morpheus is built as an application of containerized services​ and can be run using `docker-compose` for testing and development​.
 
 ### Services
 The reference architecture is composed of the following services:​
@@ -162,12 +159,5 @@ From the `examples/digital_fingerprinting/production` dir, run:
 docker compose up mlflow
 ```
 
-### Running via Kubernetes​
-#### System requirements
-* [Kubernetes](https://kubernetes.io/) cluster configured with GPU resources​
-* [NVIDIA GPU Operator](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/gpu-operator) installed in the cluster
-
-> **Note:**  For GPU Requirements refer to the [Getting Started](../../getting_started.md#requirements) guide.
-
 ## Customizing DFP
 For details on customizing the DFP pipeline refer to [Digital Fingerprinting (DFP) Reference](./6_digital_fingerprinting_reference.md).

docs/source/extra_info/glossary.md

@@ -19,7 +19,7 @@ limitations under the License.
 
 <!-- Please keep these sorted alphabetically -->
 ## MLflow Triton Plugin
-A Docker container, allowing the deployment of models in [MLflow](https://mlflow.org/) to [Triton Inference Server](#triton-inference-server). Information on building this container is available in the [`models/mlflow/README.md`](https://github.com/nv-morpheus/Morpheus/blob/branch-25.02/models/mlflow/README.md) document.
+[MLflow](https://mlflow.org/) plugin for deploying your models from MLflow to [Triton Inference Server](#triton-inference-server). Refer to [`mlflow-triton-plugin`](https://github.com/triton-inference-server/server/tree/main/deploy/mlflow-triton-plugin) for more information.
 
 ## module
 A Morpheus module is a type of work unit that can be utilized in the Morpheus stage and can be registered to a MRC segment module registry. Modules are beneficial when there is a possibility for the work-unit to be reused.

examples/digital_fingerprinting/production/README.md

@@ -24,7 +24,6 @@ Key Features:
  * Uses a model store to allow the training and inference pipelines to communicate
  * Organized into a `docker compose` deployment for easy startup
  * Contains a Jupyter notebook service to ease development and debugging
- * Can be deployed to Kubernetes using provided Helm charts
  * Uses many customized stages to maximize performance.
 
 ## Building and Running via `docker compose`