Build business-critical machine learning models at scale
Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools.
This trusted AI learning platform is designed for responsible AI applications in machine learning.
Accelerate time to value
Build machine learning models leveraging powerful AI infrastructure and orchestrate AI workflows with prompt flow.
Collaborate and streamline MLOps
Quick ML model deployment, management, and sharing for cross-workspace collaboration and MLOps.
Develop with confidence
Built-in governance, security, and compliance for running machine learning workloads anywhere.
Design responsibly
Responsible AI to build explainable models using data-driven decisions for transparency and accountability.
Key service capabilities for the full machine learning lifecycle
Data preparation
Quickly iterate on data preparation at scale on Apache Spark clusters within Azure Machine Learning, interoperable with Azure Databricks.
Feature store
Increase agility in shipping your models by making features discoverable and reusable across multiple workspaces with managed feature store.
Collaborative notebook
Launch your notebook in Jupyter Notebook or Visual Studio Code for a rich development experience, including debugging and support for Git source control.
Automated machine learning
Rapidly create accurate models for classification, regression, time-series forecasting, natural language processing tasks, and computer vision tasks with automated machine learning.
Drag-and-drop machine learning
Use machine learning tools such as designer for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.
Responsible AI
Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness.
Registries
Use organization-wide repositories to store and share models, pipelines, components, and datasets across multiple workspaces. Capture lineage and govern data using the audit trail feature.
Managed endpoints
Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.
Build business-critical machine learning models at scale
Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. This trusted AI learning platform is designed for responsible AI applications in machine learning.
Collaborate and streamline model management with MLOps
Streamline the deployment and management of thousands of models in multiple environments using MLOps. Deploy and score ML models faster with fully managed end points for batch and real-time predictions. Use repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). Share and discover machine learning artifacts across multiple teams for cross-workspace collaboration using registries and managed feature store. Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance.
Use responsible AI practices throughout the lifecycle
Evaluate machine learning models with reproducible and automated workflows to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis. Make real-life interventions with causal analysis in the Responsible AI dashboard and generate a scorecard at deployment time. Contextualize responsible AI metrics for both technical and non-technical audiences to involve stakeholders and streamline compliance review.
Build enterprise-grade solutions on a hybrid platform
Put security first across the machine learning lifecycle using the built-in data governance in Microsoft Purview. Take advantage of the comprehensive security capabilities spanning identity, data, networking, monitoring, and compliance, all tested and validated by Microsoft. Secure solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and private IP addresses. Train and deploy models anywhere, from on premises to multicloud, to meet data sovereignty requirements. Govern with confidence using built-in policies and compliance with 60 certifications, including FedRAMP High and HIPAA.
