Azure Machine Learning designer

Visually build, test, and deploy machine learning models

Visual machine learning to accelerate productivity

Drag-and-drop interface to speed up model building and deployment for the entire data science team, from beginners to professionals.

Connect to any data source and prepare and preprocess data using a variety of built-in modules

Build and train models visually using the latest machine learning and deep learning algorithms

Use drag and drop modules to validate and evaluate models

Deploy and publish real-time or batch inference endpoints with a few clicks

Connect and prepare data with ease

Drag and drop a registered dataset, connect to various data sources including Azure blob, Azure Data Lake, Azure SQL or upload from a local file. One-click to preview and visualize the data profile. Preprocess data using a rich set of built-in modules for data transformation and feature engineering.

Build and train models without writing code

Build and train machine learning models with state-of-the art machine learning and deep learning algorithms, including those for computer vision, text analytics, recommendation and anomaly detection. Drag and drop modules for no-code models or customize using Python and R code.

Validate and evaluate model performance

Interactively run machine learning pipelines.

Cross-validate models and datasets for accuracy.

Access data visualizations to evaluate models with a few clicks. Perform quick root cause analysis using the graphs, preview logs and outputs for debugging and troubleshooting.

Deploy models and publish endpoints with a few clicks

Deploy models for real-time and batch inferencing as REST endpoints to your environment, with a few clicks. Automatically generate scoring files and the deployment image. Models and other assets are stored in the central registry for MLOps tracking and lineage.

Azure Machine Learning designer resources and documentation

Get started with Azure Machine Learning designer

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