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Azure Machine Learning designer

Azure Machine Learning designer

Visually build, test, and deploy machine learning models

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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 Storage, Azure Data Lake Storage, Azure SQL or upload data from a local file. Preview and visualise the data profile in just one click. 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, recommendations and anomaly detection. Drag and drop modules for no-code models or customise using Python and R code.

Validate and evaluate model performance

Interactively run machine learning pipelines. Cross-validate models and datasets for accuracy. Access data visualisations to evaluate models with a few clicks. Perform quick-root cause analysis using 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 machine learning operations (MLOps) tracking and lineage.