Automated machine learning

Automatically build machine learning models with speed and scale

Easily build highly accurate machine learning models

Empower professional and non-professional data scientists to build machine learning models rapidly. Automate time-consuming and iterative tasks of model development using breakthrough research—and accelerate time to market.

An example of an accurate machine learning model

Automatically build and deploy predictive models using the no-code UI or using the SDK

Rapidly create accurate models customized to your data and refined by a wide array of algorithms and hyperparameters

Increase productivity with easy data exploration and intelligent feature engineering using deep neural networks

Build responsible ML solutions with model interpretability, and fine-tune your models to improve accuracy

Accurately forecast future business outcomes with popular time series models and deep learning

Build models your way

Accelerate model creation with the Automated machine learning no-code UI, or SDK. Customize your models quickly and apply control settings to iterations, thresholds, validations, blocked algorithms, and other experiment criteria. Use built-in capabilities for common machine learning tasks like classification, regression, and time-series forecasting, to handle large datasets and improve model scores.

Control the model building process

Automated machine learning intelligently selects from a wide array of algorithms and hyperparameters to help build highly accurate models. Discover common errors and inconsistencies in your data through guardrails, and better understand recommended actions and apply them automatically. Use intelligent stopping to save time on compute and prioritize the primary metric to speed results.

Improve productivity with automatic feature engineering

Use built-in capabilities for common machine learning tasks like classification, regression, and time-series forecasting, including deep neural network support, to handle large datasets and improve model scores. Utilize the automated feature selection and new feature generation capabilities to save time and build highly accurate models. Automated-ML now includes BERT deep learning architecture for text data featurization in 100 languages, which is available through the UI as well as the Notebooks.

Understand models better

Built-in support for experiment run-summaries and detailed metrics visualizations help you understand models and compare model performance. Model interpretability helps evaluate model fit for raw and engineered features and provides insights into feature importance. Discover patterns, perform what-if analyses, and develop deeper understanding of models to support transparency and trust in your business.

Support a variety of machine learning tasks

Get support for essential machine learning applications such as classification, regression, and time series forecasting, including special built-in featurizers to configure each task. Use classification techniques for supervised learning, where common applications include fraud detection, handwriting recognition etc. Build regression models to predict numerical values such as such as price prediction. Or use time series forecasting to build models that consider trends and seasonality. Evaluate a variety of popular time series models including ARIMA and Prophet and other deep learning models.

Build your machine learning skills with Azure

Learn more about machine learning on Azure and participate in hands-on tutorials with this 30-day learning journey. At the end of this learning journey, you'll be prepared to take the Azure Data Scientist Associate Certification.

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