diff --git a/README.md b/README.md
index a1b7955..614f0bd 100644
--- a/README.md
+++ b/README.md
@@ -79,7 +79,7 @@ This command will start a multi-container Docker project, setting up and startin
 
 ## 🌐 Viewing Prefect Cloud
 
-The app service provides a link [https://app.prefect.cloud/auth/resume](https://app.prefect.cloud/auth/resume) to Prefect Cloud. Open this link to access the Prefect Cloud UI, which offers detailed insights into deployments and previous runs. While the deployment is scheduled to run hourly, you can manually initiate a run by clicking 'Run' for immediate execution.
+The app service provides a link [https://app.prefect.cloud/auth/resume](https://app.prefect.cloud/auth/resume) to Prefect Cloud. Open this link to access the Prefect Cloud UI, which offers detailed insights into deployments and previous runs. While the deployment is scheduled to run every 10 minutes, you can manually initiate a run by clicking 'Run' for immediate execution.
 
 ![Prefect Cloud UI](images/prefect.png)
 
@@ -113,7 +113,7 @@ This Bash script automates the setup and deployment process for the project. It
 
 **`run_flow.py`**
 
-This script initiates and executes the Prefect flow, orchestrating the entire machine learning pipeline from data preprocessing to model registration. It ensures each step is executed sequentially and correctly, running the pipeline on an hourly schedule.
+This script initiates and executes the Prefect flow, orchestrating the entire machine learning pipeline from data preprocessing to model registration. It ensures each step is executed sequentially and correctly, running the pipeline on a 10 minute schedule.
 
 **`data_preprocess.py`**
 
diff --git a/src/run_flow.py b/src/run_flow.py
index 2f35da9..fc85fbe 100644
--- a/src/run_flow.py
+++ b/src/run_flow.py
@@ -113,8 +113,10 @@ def ml_workflow():
 
 # Serve the flow with a schedule
 if __name__ == "__main__":
+    # Serve the flow with a schedule, running every 10 minutes
+    print("Serving the flow with a schedule...")
     ml_workflow.serve(
         name="ml-workflow-deployment",
         parameters={},
-        interval=timedelta(hours=1).total_seconds(),  # Set the interval in seconds
+        interval=timedelta(minutes=10).total_seconds(),  # Run every 10 minutes
     )