microsoft--promptflow/docs/cloud/index.md

Cloud

Prompt flow streamlines the process of developing AI applications based on LLM, easing prompt engineering, prototyping, evaluating, and fine-tuning for high-quality products.

Transitioning to production, however, typically requires a comprehensive LLMOps process, LLMOps is short for large language model operations. This can often be a complex task, demanding high availability and security, particularly vital for large-scale team collaboration and lifecycle management when deploying to production.

To assist in this journey, we've introduced Azure AI, a cloud-based platform tailored for executing LLMOps, focusing on boosting productivity for enterprises.

• Private data access and controls
• Collaborative development
• Automating iterative experimentation and CI/CD
• Deployment and optimization
• Safe and Responsible AI

Transitioning from local to cloud (Azure AI)

In prompt flow, You can develop your flow locally and then seamlessly transition to Azure AI. Here are a few scenarios where this might be beneficial:

Scenario	Benefit	How to
Collaborative development	Azure AI provides a cloud-based platform for flow development and management, facilitating sharing and collaboration across multiple teams, organizations, and tenants.	Submit a run using pfazure, based on the flow file in your code base.
Processing large amounts of data in parallel pipelines	Transitioning to Azure AI allows you to use your flow as a parallel component in a pipeline job, enabling you to process large amounts of data and integrate with existing pipelines.	Learn how to Use flow in Azure ML pipeline job.
Large-scale Deployment	Azure AI allows for seamless deployment and optimization when your flow is ready for production and requires high availability and security.	Use pf flow build to deploy your flow to Azure App Service.
Data Security and Responsible AI Practices	If your flow handling sensitive data or requiring ethical AI practices, Azure AI offers robust security, responsible AI services, and features for data storage, identity, and access control.	Follow the steps mentioned in the above scenarios.

For more resources on Azure AI, visit the cloud documentation site: Build AI solutions with prompt flow.

:caption: Flow
:maxdepth: 2
azureai/manage-flows
azureai/run-promptflow-in-azure-ai
azureai/create-run-with-compute-session
azureai/use-flow-in-azure-ml-pipeline
azureai/generate-test-data-cloud.md

:caption: Deployment
:maxdepth: 2
azureai/deploy-to-azure-appservice

:caption: FAQ
:maxdepth: 2
azureai/faq
azureai/consume-connections-from-azure-ai
azureai/runtime-change-log.md