How to deploy streamlit to Azure?

Please refer me with any best tutorial (video/blog…) out there, which can help me deploy my first streamlit project!!

Hey @chan,

Check out the following resources:

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  1. Create an Azure Account: If you don’t already have an Azure account, sign up for one at
  2. Set up Azure Services: In the Azure portal, create the necessary resources to host your Streamlit application. The most common options include Azure App Service, Azure Container Instances, or Azure Kubernetes Service (AKS). Choose the one that best fits your requirements.
  3. Prepare your Streamlit Application: Ensure that your Streamlit application is ready for deployment. This includes having a functional requirements.txt file that lists all the necessary Python dependencies for your application.
  4. Configure Deployment: Depending on the Azure service you selected, you’ll need to configure the deployment settings.
  • Azure App Service: If you’re using Azure App Service, you can deploy your Streamlit app directly from a Git repository or using Azure CLI commands. Create an App Service and configure the deployment source and options according to your preferred method.
  • Azure Container Instances: If you’re using Azure Container Instances, you’ll need to create a Docker container image of your Streamlit application. You can use Azure Container Registry (ACR) to store your container image, or you can use public container registries like Docker Hub. Once you have the container image, you can deploy it to Azure Container Instances using the Azure CLI or Azure portal.
  • Azure Kubernetes Service (AKS): If you’re using AKS, you’ll need to create a Kubernetes deployment and service manifest for your Streamlit application. Build a container image of your application, push it to a container registry, and deploy it to AKS using the Kubernetes manifests.
  1. Deploy the Application: After you’ve configured the deployment settings, initiate the deployment process. This may involve pushing your code to the specified Git repository, running Azure CLI commands, or using deployment tools provided by Azure.
  2. Monitor and Scale: Once your Streamlit application is deployed, you can monitor its performance and scale the Azure resources accordingly to handle the desired load.

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