Hi Shraddha Bangad,
Greetings & Welcome to Microsoft Q&A forum! Thanks for posting your query!
For deploying a machine learning application, you have several options on Azure:
Azure App Service: Ideal for deploying web apps, including Streamlit applications. It supports continuous deployment and scaling.
Azure Container Instances (ACI): Suitable for running containerized applications without managing the underlying infrastructure.
Azure Kubernetes Service (AKS): Best for large-scale deployments requiring orchestration and scaling of containers.
Azure Machine Learning (Azure ML): Provides a managed platform for training, deploying, and managing machine learning models. It supports various deployment scenarios, including real-time and batch inference
Model storage:
Azure Blob Storage: Upload your fine-tuned model to Blob Storage. This is a cost-effective and scalable solution for storing large files.
Steps to Deploy a Streamlit App Integrated with the Model:
Prepare Your Streamlit App:
Ensure your app has a requirements.txt file listing all dependencies.
Create a Dockerfile if you plan to use containers.
Create an Azure App Service:
Log in to the Azure Portal and create a new Web App.
Choose Python as the runtime stack and configure deployment settings.
Deploy Your App:
Use VSCode or Azure CLI to deploy your app. You can also set up continuous deployment from a GitHub repository.
If using Docker, push your Docker image to Azure Container Registry (ACR) and deploy it using Azure App Service or AKS.
Configure the Startup Command:
For Streamlit, you might need to add a startup command to run the app on the specified port.
Managing Dependencies and Environment Setup:
Azure Machine Learning Environments: Use Azure ML environments to manage and reproduce your project's software dependencies. You can create custom environments or use curated ones provided by Azure.
Docker: If using Docker, specify all dependencies in the Dockerfile and use Docker Compose for local testing before deploying to Azure.
Scalability and Cost Optimization
Autoscaling: Configure autoscaling for your compute resources to handle varying workloads efficiently. Azure ML and AKS support autoscaling.
Cost Management: Use low-priority VMs, reserved instances, and set quotas to manage and optimize costs. Azure provides tools to monitor and control spending.
For more information Deploy Python Apps on Azure App Service.
Deploy Streamlit on Azure Web App.
Azure Blob Storage documentation.
Hope this helps. Do let us know if you have any further queries.
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