Add Workspaces & SDK section to README

- New section showcasing managed JupyterLab workspaces and Python SDK
- SDK usage examples: register, train, infer, visualize
- Add screenshot3 showing workspace notebook and plotly visualization
- Update features list and dashboard pages to reflect new platform capabilities

Author: Jovonni

README.md

@@ -32,6 +32,7 @@ A robust, flexible, and lightweight open source User & Entity Behavior Analytics
 - [Rule Canvas](#rule-canvas)
 - [Model Library](#model-library)
 - [Model Execution Sandbox](#model-execution-sandbox)
+- [Workspaces & SDK](#workspaces--sdk)
 - [Authentication and Access Control](#authentication-and-access-control)
 - [LLM Assistant](#llm-assistant)
 - [Getting Started](#getting-started)
@@ -157,12 +158,20 @@ OpenUBA v0.0.2 is a Kubernetes-native platform with a modular, cloud-native arch
 - Rule-triggered alerts linked to anomalies and cases
 - Alerts can be enabled or disabled per-rule
 
+### Workspaces & SDK
+- Launch managed JupyterLab environments from the UI with configurable hardware tiers
+- Python SDK (`pip install openuba`) for programmatic model registration, job submission, and visualization
+- Register sklearn, PyTorch, TensorFlow, and NetworkX models directly from notebooks
+- Submit training and inference jobs with real-time SSE progress streaming
+- Multi-backend visualization rendering (matplotlib, seaborn, plotly, bokeh, altair, plotnine, datashader, networkx, geopandas)
+- Workspace lifecycle managed by the K8s operator via UBAWorkspace CRDs
+
 ### Dashboard
 - Modern Next.js + shadcn/ui interface with dark mode default
 - Real-time updates via GraphQL subscriptions
 - Global time range selector, command palette, and keyboard navigation
 - Modular components with responsive layout
-- Pages: Home, Models, Anomalies, Cases, Data, Entities, Rules, Alerts, Schedules, Settings, Users
+- Pages: Home, Data, Models, Rules, Alerts, Entities, Anomalies, Cases, Workspaces, Visualizations, Dashboards, Experiments, Features, Pipelines, Jobs, Datasets
 
 ### Security and Access Control
 - JWT authentication with role-based access control (admin, manager, triage, analyst)
@@ -271,6 +280,40 @@ The custom OpenUBA operator watches for `UBATraining` and `UBAInference` custom
 
 ---
 
+## Workspaces & SDK
+
+OpenUBA includes managed JupyterLab workspaces that run as Kubernetes pods, giving analysts and data scientists a full notebook environment connected directly to the platform. From a workspace, you can register models, submit training and inference jobs, query results, and render visualizations -- all through the Python SDK.
+
+```bash
+pip install openuba
+```
+
+```python
+import openuba
+
+# register a trained sklearn model from a notebook
+openuba.register_model("ssh-anomaly-detector", model, runtime="sklearn")
+
+# submit a training job and poll until complete
+openuba.start_training(model_id, dataset_id=dataset_id)
+
+# run inference -- returns anomaly scores
+openuba.start_inference(model_id, dataset_id=dataset_id)
+
+# render a plotly visualization and push to the platform
+openuba.render(fig, viz_id=viz_id)
+```
+
+The SDK supports 9 visualization backends out of the box. Any matplotlib, seaborn, plotly, bokeh, altair, plotnine, datashader, networkx, or geopandas figure can be rendered and pushed to the platform for display in the Visualizations page.
+
+Workspaces are managed by the K8s operator via `UBAWorkspace` custom resources. Hardware tiers (`cpu-small`, `cpu-large`, `gpu-small`, `gpu-large`) control resource allocation. Each workspace gets its own persistent volume, pre-installed SDK, and API token for authenticated access to the platform.
+
+<div align="center">
+  <img src="images/screenshot3.png" width="100%" alt="OpenUBA Workspace and Visualization" />
+</div>
+
+---
+
 ## Authentication and Access Control
 
 OpenUBA v0.0.2 includes a complete authentication and role-based access control system: