Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting point for MLOps implementation in Azure.
MLOps is a set of repeatable, automated, and collaborative workflows with best practices that empower teams of ML professionals to quickly and easily get their machine learning models deployed into production. You can learn more about MLOps here:
- MLOps with Azure Machine Learning
- Cloud Adoption Framework Guidance
- How: Machine Learning Operations
The solution accelerator provides a modular end-to-end approach for MLOps in Azure based on pattern architectures. As each organization is unique, solutions will often need to be customized to fit the organization's needs.
The solution accelerator goals are:
- Simplicity
- Modularity
- Repeatability & Security
- Collaboration
- Enterprise readiness
It accomplishes these goals with a template-based approach for end-to-end data science, driving operational efficiency at each stage. You should be able to get up and running with the solution accelerator in a few hours.
- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
- For Azure DevOps-based deployments and projects:
- Azure CLI with
azure-devopsextension. - Terraform extension for Azure DevOps if you are using Terraform to spin up infrastructure
- Azure CLI with
- For GitHub-based deployments and projects:
- Git bash, WSL, or another shell script editor on your local machine
- Solution Accelerator Concepts and Structure - Philosophy and organization
- Architectural Patterns - Supported Machine Learning patterns
- Accelerator Deployment Guides - How to deploy and use the soluation accelerator with Azure DevOps or GitHub
- Quickstarts - Precreated project scenarios for demos/POCs. Azure DevOps ADO Quickstart.
- YouTube Videos: Deploy MLOps on Azure in Less Than an Hour and AI Show
This project welcomes contributions and suggestions. To learn more visit the contributing section, see CONTRIBUTING.md for details.
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.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
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