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51 lines
3.0 KiB
Markdown
51 lines
3.0 KiB
Markdown
# Cloud
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Prompt flow streamlines the process of developing AI applications based on LLM, easing prompt engineering, prototyping, evaluating, and fine-tuning for high-quality products.
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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.
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To assist in this journey, we've introduced **Azure AI**, a **cloud-based platform** tailored for executing LLMOps, focusing on boosting productivity for enterprises.
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* Private data access and controls
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* Collaborative development
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* Automating iterative experimentation and CI/CD
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* Deployment and optimization
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* Safe and Responsible AI
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## Transitioning from local to cloud (Azure AI)
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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:
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| Scenario | Benefit | How to|
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| --- | --- |--- |
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| 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](./azureai/run-promptflow-in-azure-ai.md), based on the flow file in your code base.|
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| 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](./azureai/use-flow-in-azure-ml-pipeline.md).|
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| 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](./azureai/deploy-to-azure-appservice.md).|
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| 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.|
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For more resources on Azure AI, visit the cloud documentation site: [Build AI solutions with prompt flow](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/get-started-prompt-flow?view=azureml-api-2).
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```{toctree}
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:caption: Flow
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:maxdepth: 2
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azureai/manage-flows
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azureai/run-promptflow-in-azure-ai
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azureai/create-run-with-compute-session
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azureai/use-flow-in-azure-ml-pipeline
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azureai/generate-test-data-cloud.md
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```
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```{toctree}
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:caption: Deployment
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:maxdepth: 2
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azureai/deploy-to-azure-appservice
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```
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```{toctree}
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:caption: FAQ
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:maxdepth: 2
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azureai/faq
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azureai/consume-connections-from-azure-ai
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azureai/runtime-change-log.md
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``` |