Welcome to the LLMOps workshop! This course will guide you through building, evaluating, monitoring, and deploying Large Language Model solutions efficiently using Azure AI, Azure Machine Learning Prompt Flow, Content Safety, and Azure OpenAI. Let's master LLMOps together!
The content of this workshop is broken down into 5 individual labs that build upon the last, the structure is as follows:
| Lab title | Lab description | Lab link |
|---|---|---|
| AI Studio exploration and setup | This lab will look at deploying the services needed for the AI Studio before exploring some of the key functionalities found within. | Link |
| Orchestration flows in PromptFlow | This lab will look at creating two different types of orchestration flows to help cement your understanding on how to use PromptFlow. | Link |
| Evaulating and deploying flows | This lab will look at how to evaluate an orchestration flow before looking at the steps to deploy it. | Link |
| Monitoring deployed flows | This lab will look at the ways we can monitor deployed flows. | Link |
| Automating everything: LLMOps | This lab will look at taking all the concepts covered in the previous labs, and incorporate them into an example LLMOps workflow using Azure DevOps to automate the process. | Link |
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.
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