Thanks for considering contributing to this repository!
While we are still in the early stages of development, we strongly suggest first coordinating with the developers before taking the time to write code and submit a pull request. Contributions in the form of issues and discussions are always welcome.
## Pull Requests
After coordinating through an issue or discussion, please submit a pull request with code changes. The team will review, test, and merge.
We especially encourage code contributions for automating the development of the v2 solution accelerator. Until this is in place, the review and merge process will largely be manual.
MLOps or Machine Learning Ops is a set of practices that aims to automate and operationalise the deployment and maintenance of machine learning models across various stages of the lifecycle of a Data Science process. The purpose of an MLOps process is to drive efficiency, increase repeatability and predictability, enable reuse of code and drive consistency across projects. This enables Data Science teams to deploy Machine Learning models to production reliably and efficiently.
While MLOps has many overlapping concepts with DevOps and can be seen as a derivation of DevOps, it varies significantly from DevOps due to the nature of Data Science projects. The following Microsoft articles provide a perspective on MLOps from various viewpoints:
Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as *the* starting point for MLOps implementation in Azure.
[MLOps using Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/mlops/#features)
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:
1. An Azure subscription. If you don't have an Azure subscription, [create a free account](https://aka.ms/AzureMLFree) before you begin.
## Prerequisites: Repository Overview
## 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, we do not expect that each pattern architecture will suit all organizations, however, Digital Natives or enterprises will be able to deploy an MLOps ecosystem fast, simple, reliable, modular, and secure. The time to product is measurable shorter under incerased scalability.
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.
Following are the key principles that have been applied while building the accelerator:
The solution accelerator goals are:
✅**Simplicity**
- Simplicity
- Modularity
- Repeatability
- Collaboration
- Enterprise readiness
✅**Segregation of duties & Security**
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.
This repo provides a templatised approach for the end-to-end Data Science process and focuses on driving efficiency at each stage. For example, it can take a significant amount of time to bootstrap a new Data Science project, hence the repo provides templates that can be reused to establish a cookie cutter approach for the bootstrapping process to shorten the process from days to hours or minutes.
The best way to consume this accelerator will be to choose a complex use case that reflects most of your organisation’s needs from a Data Science perspective and start adjusting this accelerator to accommodate those requirements. The first use case may take longer to deliver, however, once the process has been ironed out, subsequent use cases can be onboarded in a matter of days if not hours.
Following the demo helps to understand the concept of the solution accelerator, architectual pattern, and ongoing work extending the solution accelerator to other patterns. Feel free to replace the inner loop model with your model and rerun accordingly.
This project welcomes contributions and suggestions. To learn more visit the contributing section, see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
@ -93,7 +82,6 @@ When you submit a pull request, a CLA bot will automatically determine whether y
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto: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
Cody Peterson
GitHub