Azure MLOps (v2) solution accelerator

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

Prerequisites

1. An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.

Project overview

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
• 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.

👤 Getting started: Azure Machine Learning - classical machine learning demo

The demo follows the classical machine learning pattern with Azure Machine Learning.

‼️ Please follow the instructions to execute the demo accordingly: Quickstart ‼️

‼️ Please submit any issues here: Issues ‼️

📐 Pattern Architectures: Key concepts

Link	AI Pattern
Pattern AzureML CML	Azure Machine Learning - Classical Machine Learning
Pattern AzureML CV	Azure Machine Learning - Computer Vision
[TBD]	Azure Machine Learning - Natural Language Processing
[TBD]	Azure Machine Learning / Azure Databricks - Classical Machine Learning
[TBD]	Azure Machine Learning / Azure Databricks - Computer Vision
[TBD]	Azure Machine Learning / Azure Databricks - Natural Language Processing
[TBD]	Azure Machine Learning - Edge AI

📯 (Coming Soon) One-click deployments

📯 MLOps infrastructure deployment

Name	Description	Try it out
Outer Loop	Default Azure Machine Learning outer infrastructure setup	[DEPLOY BUTTON]
[TBD]	Default Responsible AI for Classical Machine Learning	[DEPLOY BUTTON]
Feature Store FEAST	Default Feature Store using FEAST	[DEPLOY BUTTON]

📯 MLOps use case deployment

Name	AI Workload Type	Services	Try it out
classical-ml	Classical machine learning	Azure Machine Learning	[DEPLOY BUTTON]
[TBD]	Computer Vision	Azure Machine Learning	[DEPLOY BUTTON]
[TBD]	Natural Language Processing	Azure Machine Learning	[DEPLOY BUTTON]
[TBD]	Classical machine learning	Azure Machine Learning, Azure Databricks	[DEPLOY BUTTON]
[TBD]	Computer Vision	Azure Machine Learning, Azure Databricks	[DEPLOY BUTTON]
[TBD]	Natural Language Processing	Azure Machine Learning, Azure Databricks	[DEPLOY BUTTON]
[TBD]	Edge AI	Azure Machine Learning	[DEPLOY BUTTON]

Contributing

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.

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 opencode@microsoft.com with any additional questions or comments.

Trademarks

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.