This repo contains samples of machine learning (ML) projects we have often seen in the industry. For example, using image classification or object detection for quality control and safety monitoring. We are using these samples to showcase how to build, deploy, and operationalize ML projects in production with good engineering practices such as unit testing, CI/CD, model experimentation tracking, and observability in model training and inferencing.
Samples in this project leverage the basic ideas used in MLOpsPython. While MLOpsPython lays the foundation for operationalizing ML, we aim to provide representative samples and docs to
This repo contains sample code and definition of Azure DevOps pipelines for CI/CD. These pipelines run in this Azure DevOps project. ML pipelines run in the Azure environment deployed using the Azure DevOps pipelines.
├─ common # contains tools and code cross-cutting samples
│
├─ docs # how-tos and best practices
│
├─ samples # each sample may have different folders, below is a typical example
│ ├─ <sample 1>
│ │ ├─ .devcontainer # VSCode dev container you can optionally use for development
│ │ ├─ data # store data for the sample if necessary
│ │ ├─ devops_pipelines # Azure DevOps CI/CD pipeline definition
│ │ ├─ environment_setup # Sample specific additional infrastructure setup
│ │ │ ├─ azureml_environment # Azure ML Environment Dockerfile
│ │ │ └─ provisioning # Additioal setup scripts for the sample
│ │ ├─ local_development # scripts for creating a local dev environment without having to have a VSCode dev container
│ │ ├─ ml_model # code for building the ML model
│ │ ├─ ml_service # code for building ML pipelines
│ │ ├─ tests # unit test code
│ │ │ ├─ ml_model
│ │ │ └─ ml_service
│ │ └─ README.md # explains what the sample is demonstrating and how to run it
│ └─ <sample 2>
│ └─ # same as above
├─ .gitattributes
├─ .gitignore
├─ LICENSE
└─ README.md
Check out the samples and the docs. Once you are ready to run the code, follow these steps:
local_development
folder and run
a script to create a conda environment for development. Refer to each sample's README for details.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.