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Microsoft Azure Machine Learning

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Azure Machine Learning
  • 24×7 remote and on-site support
  • Multi-vendor solutions & services
  • Local billing in 33+ countries
  • Competitive Price

Build business-critical machine learning models at scale
Empower data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. Accelerate time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. Innovate on a secure, trusted platform designed for responsible AI applications in machine learning.

  • Rapidly build and train models
    Use the studio development experience to access built-in tools and best-in-class support for open-source frameworks and libraries.
  • Deliver responsible solutions
    Develop models for fairness and explainability, use them responsibly when deployed, and govern to fulfill lineage and audit compliance requirements.
  • Operationalize at scale
    Deploy ML models quickly and easily, and manage and govern them efficiently, with MLOps.
  • 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 the studio capability, a development experience that supports all machine learning 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 machine learning 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 to go from local to cloud training seamlessly, and autoscale with powerful cloud-based CPU and GPU clusters.

Operationalize at scale with machine learning operations (MLOps)
Streamline the deployment and management of thousands of models on premises, at the edge, and in multicloud environments using MLOps. Deploy and score ML models faster with fully managed endpoints for batch and real-time predictions. Use repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance. Throughout the lifecycle, enable auditability and governance with built-in tracking and lineage for all machine learning artifacts.

Deliver responsible machine learning solutions
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 and policies with causal analysis in the responsible AI dashboard and generate a scorecard at deployment time. Export the scorecard to a PDF to contextualize responsible AI metrics, and share it with both technical and non-technical audiences to involve stakeholders and streamline compliance review.

Innovate on a hybrid platform that's more secure and compliant
Increase security across the machine learning lifecycle with comprehensive capabilities spanning identity, authentication, data, networking, monitoring, governance, and compliance. Build more secure machine learning solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and end-to-end private IP addresses. Train and deploy models on premises to meet data sovereignty requirements. Manage governance with built-in policies and streamline compliance with a comprehensive portfolio spanning 60 certifications, including FedRAMP High and HIPAA.

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