What Is Azure Machine Learning?

Develop and deploy enterprise-level Machine Learning Models accurately and instantly to support your process optimization and critical business practices. Accelerate your model deployment with Azure Machine Learning Services, scale your MLOps (Machine Learning Operations), optimize operational efficiency, and your operational costing while enabling high end-security to ensure uninterrupted and efficient business flow.

Overview

azure machine learning benefits

With Azure Machine Learning solutions, enable your deployment team to seamlessly prepare data sets, efficiently develop Machine Learning models, bring innovation in your critical business practices wrapped with enterprise-grade security and reliability. Streamline your entire Machine Leaning models deployment whether on-premises, on-cloud, or at Azure IoT Edge.

Develop efficient ML models instantly to ensure effective analysis, predictions, and forecasting. With the help of model interpretability, understand the process flow of models’ development.

Rapidly build and train models

Use the studio development experience to access integrated tools and best-in-class support for open-source frameworks and libraries.

Operationalise at scale

Deploy models with a single click and manage and govern them efficiently with MLOps.

Deliver responsible solutions

Understand and protect data and models, build for fairness and improve model quality.

Innovate on a more secure hybrid platform

Run machine learning workloads anywhere with built-in governance, security and compliance.

Accelerate time to value with rapid and accurate model development

Improve productivity with studio, the development experience that supports all ML tasks to build, train and deploy models. Collaborate with Jupyter notebooks using built-in support for popular open-source frameworks and libraries. Create accurate models quickly with automated ML, using feature engineering and hyperparameter-sweeping capabilities. Access the debugger, profiler and explanations to improve model performance as you train. Use deep Visual Studio Code integration to go from local to cloud training seamlessly and autoscale with powerful cloud-based CPU and GPU clusters.