Azure Machine Learning, Microsoft’s cloud service for building and running machine learning models. Screenshot from azure.microsoft.com.
Azure Machine Learning is Microsoft’s cloud service for building, training and deploying machine learning models, used through a web app called Azure Machine Learning studio. You can work with code in notebooks, drag and drop in the designer, or let automated ML find a good model for you.
Machine learning means teaching a computer to make predictions from examples, such as forecasting sales or spotting fraud. Azure Machine Learning has no charge of its own: you pay for the computers and storage it uses. A free Azure account gives you credit to try it.
It works with popular open-source tools such as PyTorch, TensorFlow and scikit-learn, and it runs in your web browser on Windows, Mac or Linux.
Below: the main tools with screenshots, how a project flows, a step-by-step start, what it costs, and how it differs from Microsoft Foundry. Questions? Reach out to us directly.
What Is Azure Machine Learning?
Azure Machine Learning is a workshop for the whole life of a model: preparing data, training, testing, deploying it so apps can use it, and watching it once it is live.
Everything lives in a workspace, a shared space where a team keeps its data, code, experiments, models and computers. Microsoft calls the full cycle MLOps, short for machine learning operations.
Data scientists use it with Python. Analysts and beginners can get results without writing code, through the designer and automated ML.
New to Azure itself? Our guide to Microsoft Azure explains accounts, subscriptions and the portal first.
Key Azure Machine Learning Tools
These screenshots come from Microsoft Learn and show what you will see in Azure Machine Learning studio.
Azure Machine Learning studio
The studio at ml.azure.com is the home for everything. The left menu splits into Author (notebooks, automated ML, designer), Assets (data, jobs, models, endpoints) and Manage (compute).
Nothing to install: it runs in your browser.
Designer
The designer lets you build a training pipeline by dragging boxes onto a canvas and connecting them: load data, clean it, train a model, score it.
It is the easiest way to see how a machine learning pipeline fits together.
Automated ML
Give automated ML a table of data and the column you want to predict, and it tries many algorithms and settings for you, then ranks the results.
It handles classification (sorting into groups), regression (predicting numbers), forecasting, and some image and text tasks.
Experiments and metrics
Every training run is saved with its code, settings and results. Charts show how accuracy and error changed while the model trained, so you can compare runs side by side.
Azure Machine Learning works with MLflow, a popular open-source tool for tracking experiments.
Understand why a model decides
The model explanation dashboard shows which inputs mattered most to a model’s predictions. That helps you catch a model that learned the wrong lesson before it reaches customers.
Notebooks
Write and run Python in Jupyter notebooks inside the studio, or open the same files in VS Code on your PC.
Endpoints
Deploy a model so apps can send it data and get predictions back, either in real time or in large batches.
Pipelines
Chain steps such as data prep, training and evaluation, then run them on a schedule.
Model catalog
Browse hundreds of ready-made models from Microsoft, OpenAI, Meta, Mistral, Hugging Face and others.
Heads-up on prompt flow: Microsoft is retiring prompt flow, its tool for building apps on large language models, on April 20, 2027, and recommends Microsoft Agent Framework for new work.
How a Machine Learning Project Works
Most projects go round the same loop. Azure Machine Learning has a tool for each stage.
1 Define the task: decide what you want to predict and how you will judge success.
2 Prepare the data: connect your data, clean it and explore it in a notebook.
3 Train and validate: train models with code, the designer or automated ML, and compare the runs.
4 Deploy: publish the best model to an endpoint your app can call.
5 Monitor: watch its accuracy over time and retrain when the data changes.
Step by Step: Get Started
You need an Azure account. Then this takes about fifteen minutes.
1 Create a workspace: go to ml.azure.com, sign in and select Create workspace. Pick a name, subscription, resource group and region.
2 Add a compute instance: under Compute, create a small CPU machine. It is your personal cloud PC for notebooks. Stop it when you are not using it, so it does not keep billing.
3 Try a sample: open Notebooks, then Samples, and clone a getting-started notebook to run cell by cell.
4 Or try automated ML: select Automated ML, then New Automated ML job, upload a CSV file and choose the column to predict.
How Much Does Azure Machine Learning Cost?
Microsoft says there is no additional charge to use Azure Machine Learning. You pay for the Azure services it uses underneath.
The bill that surprises people is a compute instance left running. Set an idle shutdown on it, and check prices on the Azure Machine Learning pricing page. Our guide to reducing Azure costs has more ways to save.
Azure Machine Learning vs Microsoft Foundry
Both can work with AI models, which causes confusion. The short version: Azure Machine Learning is for training your own models, and Foundry is for building apps and agents on top of existing models.
Frequently Asked Questions
Do I need to know how to code to use Azure Machine Learning?
Is there a free tier for Azure Machine Learning?
What happened to Azure Machine Learning Studio (classic)?
Which Python SDK should I use?
Can I use Azure Machine Learning from VS Code on Windows?
Support and Community
- Azure Machine Learning, Microsoft’s own product page.
- Azure Machine Learning documentation on Microsoft Learn, with tutorials for every tool.
- Microsoft Q&A for Azure Machine Learning, where Microsoft engineers and users answer questions.
- r/AZURE on Reddit, a large community for Azure questions and tips.
More Windows software guides: What Is Microsoft Azure? · Azure AI Search · Azure Monitor · Reducing Azure Costs · Azure Quantum
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