Azure Machine Learning Tutorials Github

There was an error while loading. Please reload this page. Welcome to the Azure Machine Learning examples repository! The azureml-examples repository contains examples and tutorials to help you learn how to use Azure Machine Learning (Azure ML) services and features. If you're getting started with Azure ML, consider working through our tutorials for the v2 Python SDK. You may also want to read through our documentation.

The sdk/ folder houses the examples for the Azure ML SDKs across several languages. We have an extensive collection of examples for the Azure ML Python SDK v2 in sdk/python. This repository contains the hands-on lab exercises for the Microsoft Learning Paths exploring Azure Machine Learning. The Learning Paths consists of self-paced modules on Microsoft Learn. The labs are designed to accompany the learning materials and enable you to practice using the technologies described them. You can view the instructions for the lab exercises at https://microsoftlearning.github.io/mslearn-azure-ml/.

To support this course, we will need to make frequent updates to the course content to keep it current with the Azure services used in the course. We are publishing the lab instructions and lab files on GitHub to allow for open contributions between the course authors and MCTs to keep the content current with changes in the Azure platform. We hope that this brings a sense of collaboration to the labs like we've never had before - when Azure changes and you find it first during a live delivery, go ahead and make... Anyone can submit a pull request to the code or content in the GitHub repository, Microsoft and the course author will triage and include content and lab code changes as needed. This tutorial is an introduction to some of the most used features of the Azure Machine Learning service. In it, you will create, register and deploy a model.

This tutorial will help you become familiar with the core concepts of Azure Machine Learning and their most common usage. You'll learn how to run a training job on a scalable compute resource or local on premise machine,create a machine learning pipeline, we will show you how to then deploy the created model, and... You'll create a training script to handle the data preparation, train and register a model. Once you train the model, you'll deploy it as an endpoint, then call the endpoint for inferencing. We will follow the Enterprise Machine Learning Lifecycle Patterns for onborading classical machine learning scenario on tabular data in AML Platform: The inner loop element consists of your iterative Data Science workflow executed within a dedicated, secure Machine Learning workspace.

A typical workflow is illustrated in the diagram below. It proceeds from data ingestion, exploratory data analysis, experimentation, model development and evaluation, to registration of a candidate model for production. This modular element as implemented in the MLOps v2 accelerator is agnostic and adaptable to the process your data science team uses to develop models. This example shows you generic AI / ML workflow through lifecycle - exploration, train, tune, and publishing - with Azure Machine Learning (AML) API. There exist 2 options to run Azure Machine Learning (AML) API - CLI/YAML and Python SDK. Note : When you are new to Azure Machine Learning, use v2 API.

You can also use raw REST API for invoking AML API. Create new "Machine Learning" resource in Azure Portal . Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories.

APPLIES TO: Python SDK azure-ai-ml v2 (current) This tutorial is an introduction to some of the most used features of the Azure Machine Learning service. In it, you create, register, and deploy a model. This tutorial helps you become familiar with the core concepts of Azure Machine Learning and their most common usage. You learn how to run a training job on a scalable compute resource, then deploy it, and finally test the deployment. Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian...

We have a Discord learn with AI series ongoing, learn more and join us at Learn with AI Series from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science. 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍 Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!

Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'. There was an error while loading. Please reload this page. Access to this page requires authorization.

You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current) Azure Machine Learning allows you to integrate with GitHub Actions to automate the machine learning lifecycle. Some of the operations you can automate are:

In this article, you learn about using Azure Machine Learning to set up an end-to-end MLOps pipeline that runs a linear regression to predict taxi fares in NYC. The pipeline is made up of components, each serving different functions, which can be registered with the workspace, versioned, and reused with various inputs and outputs. You're going to be using the recommended Azure architecture for MLOps and Azure MLOps (v2) solution accelerator to quickly set up an MLOps project in Azure Machine Learning. Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization.

You can try changing directories. Git is a popular version control system that allows you to share and collaborate on your projects. This article explains how Azure Machine Learning can integrate with a local Git repository to track repository, branch, and current commit information as part of a training job. Azure Machine Learning fully supports Git repositories for tracking work. You can clone repositories directly onto your shared workspace file system, use Git on your local workstation, or use Git from a continuous integration and continuous deployment (CI/CD) pipeline. When you submit an Azure Machine Learning training job that has source files from a local Git repository, information about the repo is tracked as part of the training job.

Because the information comes from the local Git repo, it isn't tied to any specific central repository. Your repository can be cloned from any Git-compatible service, such as GitHub, GitLab, Bitbucket, or Azure DevOps.

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