MLOpsPython

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MLOpsPython is an Open Source DevOps & Infrastructure tool that demonstrates how to build a Continuous Integration and Continuous Delivery pipeline for ML/AI projects. It leverages Azure Machine Learning and Azure DevOps for model management and operationalization.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is MLOpsPython?

MLOpsPython provides a comprehensive set of code samples and pipeline definitions for establishing an MLOps workflow using Azure ML Services and Azure DevOps. This project illustrates how to automate an end-to-end ML/AI workflow, covering continuous integration (CI) and continuous delivery (CD) for machine learning models. It includes tasks for data sanity tests, unit tests, model training on various compute targets, model version management, evaluation, selection, and deployment as a real-time web service. The solution supports staged deployment to QA/production environments and integration testing, making it adaptable for various AI scenarios beyond its scikit-learn diabetes dataset foundation.

Best used for

Ideal for developers and data scientists who need to establish robust CI/CD pipelines for machine learning projects, operationalize models efficiently, and manage their lifecycle within Azure. Especially valuable for teams looking to automate model training, versioning, and deployment to production environments.

Common actions

automate ML workflows

deploy ML models

manage ML pipelines

integrate ML with DevOps

face swappinggithub copilot"AI Agents"deepfakeworkflowsopen-sourcecollaborationlow-code/no-codeautomated workflow

Capabilities

Key features

CI/CD pipeline automation
Model training on Azure
Model version management
Real-time web service deployment
Staged deployment
Data sanity tests
Unit testing

Target Audience

developerdata scientist

Integrations

azure-machine-learningazure-devops

Pricing & Plans

Open Source

Free

FAQs

What Azure services are required to use MLOpsPython?

MLOpsPython primarily leverages Azure Machine Learning Services and Azure DevOps. An active Azure subscription with at least contributor access is a prerequisite for deploying and utilizing the solution effectively within your environment.

Can MLOpsPython be adapted for other AI scenarios or build systems?

Yes, while the provided solution is built on the scikit-learn diabetes dataset, its architecture is designed to be adaptable. It can be easily modified for various AI scenarios and integrated with other popular build systems like Jenkins and Travis.

Does MLOpsPython support staged deployments for models?

Yes, the build pipelines within MLOpsPython include capabilities for staged deployment. This allows for deploying models to QA/production environments in a controlled manner, facilitating thorough testing and validation before full release.

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Open Source & Models Code Assistants No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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Also listed in

This tool also appears in

Productivity & Business › Workflow Automation

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