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.

Tracing

• Tracing from local to cloud
  • Installing the package
  • Set cloud destination
  • Storage
  • Set different destination
  • Disable logging to cloud
  • Disable tracing feature

Flow

• Manage flows
  • Create a flow
  • List flows
• Run prompt flow in Azure AI
  • Prerequisites
  • Create necessary connections
  • Submit a run to workspace
  • View the run in workspace
  • Next steps
• Create run with compute session
  • Create a run with compute session
  • Specify pip requirements for compute session
  • Customize compute session
  • Next steps
• Use flow in Azure ML pipeline job
  • Register a flow as a component
  • Use a flow in a pipeline job
  • Environment of the component
  • Difference across flow in prompt flow and pipeline job
  • Component ports and run settings
• How to generate test data in cloud based on documents
  • Prerequisites
  • Generate test data at cloud

Deployment

• Deploy to Azure App Service
  • Build a flow as docker format app
  • Deploy with Azure App Service
  • View and test the web app
  • Next steps

FAQ

• Frequency asked questions (FAQ)
  • How to create feedback for azure portal experience
  • Token expired when run pfazure cmd
• Consume connections from Azure AI
  • Different connection providers
  • Next steps
• Change log of default runtime image
  • Runtime image
  • Change log