diff --git a/train_and_deploy/RUN.md b/train_and_deploy/RUN.md
new file mode 100644
index 00000000..6c43eef2
--- /dev/null
+++ b/train_and_deploy/RUN.md
@@ -0,0 +1,97 @@
+# Train and Deploy ML Project
+
+This README provides step-by-step instructions for running the training and deployment pipeline using ZenML.
+
+## Prerequisites
+
+- Git installed
+- Python environment set up
+- ZenML installed
+- Access to the ZenML project repository
+
+## Project Setup
+
+1. Clone the repository and checkout the feature branch:
+```bash
+git clone git@github.com:zenml-io/zenml-projects.git
+git checkout feature/update-train-deploy
+```
+
+2. Navigate to the project directory:
+```bash
+cd train_and_deploy
+```
+
+3. Initialize ZenML in the project:
+```bash
+zenml init
+```
+
+## Running the Pipeline
+
+### Training
+
+You have two options for running the training pipeline:
+
+#### Option 1: Automatic via CI
+Make any change to the code and push it. This will automatically trigger the CI pipeline that launches training in SkyPilot.
+
+#### Option 2: Manual Execution
+1. First, set up your stack. You can choose between:
+   - Local stack (uses local orchestrator):
+     ```bash
+     zenml stack set LocalGitGuardian
+     ```
+   - Remote stack (uses SkyPilot orchestrator):
+     ```bash
+     zenml stack set RemoteGitGuardian
+     ```
+
+2. Run the training pipeline:
+```bash
+python run --training
+```
+
+### Model Deployment
+
+1. After training completes, deploy the model:
+```bash
+python run --deployment
+```
+
+Note: At this stage, the deployment is done to the model set as "staging" (configured in `target_env`), and the model is deployed locally using BentoML.
+
+2. Test the deployed model:
+```bash
+python run --inference
+```
+
+### Production Deployment
+
+If the staging model performs well and you want to proceed with production deployment:
+
+1. Deploy to Kubernetes:
+```bash
+python run --production
+```
+This pipeline will:
+- Build a Docker image from the BentoML service
+- Deploy it to Kubernetes
+
+## Additional Resources
+
+- [ZenML Projects Tenant Dashboard](https://cloud.zenml.io/organizations/fc992c14-d960-4db7-812e-8f070c99c6f0/tenants/12ec0fd2-ed02-4479-8ff9-ecbfbaae3285)
+- [Example GitHub Actions Pipeline](https://github.com/zenml-io/zenml-projects/actions/runs/12075854945/job/33676323427)
+
+## Pipeline Flow Overview
+
+1. Training → Creates and trains the model
+2. Deployment → Deploys model to staging environment (local BentoML)
+3. Inference → Tests the deployed model
+4. Production → Deploys to production Kubernetes environment
+
+## Notes
+
+- The deployment configurations are controlled by the `target_env` setting in the configs
+- Make sure you have the necessary permissions and access rights before running the pipelines
+- Monitor the CI/CD pipeline in GitHub Actions when using automatic deployment
\ No newline at end of file
diff --git a/train_and_deploy/configs/train_config.yaml b/train_and_deploy/configs/train_config.yaml
index dfcff182..249b7bac 100644
--- a/train_and_deploy/configs/train_config.yaml
+++ b/train_and_deploy/configs/train_config.yaml
@@ -23,6 +23,9 @@ settings:
       - sklearn
       - slack
       - bentoml
+  orchestrator.vm_kubernetes:
+    down: True
+    idle_minutes_to_autostop: 2
 
 # configuration of steps  
 steps: