Monday Friday 08.30 - 17.30
Accelerate the end-to-end machine learning lifecycle.
Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.
Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning
Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle
Understand your models and eliminate bias with interpretability and fairness capabilities. Protect data with differential privacy and confidential computing. Use policies, audit trials and cost management for governance and control
Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R
Accelerate the end-to-end machine learning lifecycle Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.
Boost productivity and access ML for all skills Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use the no-code designer to get started, or use built-in Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning UI, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.
Operationalize at scale with robust MLOps MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use ML pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate, and deploy machine learning models anywhere, from the cloud to the edge, to manage production ML workflows at scale in an enterprise-ready fashion.
Build responsible ML solutions Access state-of-the-art responsible ML capabilities to understand protect and control your data, models and processes. Explain model behavior during training and inferencing and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques and use confidential computing to secure ML assets. Apply policies, use lineage and manage and control resources to meet regulatory standards.
Innovate on an open and flexible platform Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, and scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter notebooks, and CLIs—or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices.
Advanced security and governance
Get the security from the ground up and build on the trusted cloud with Azure.
Protect access to your resources with granular role-based access, custom roles and built-in mechanisms for identity authentication.
Build train and deploy models securely by isolating your network with virtual networks and private links.
Manage governance with policies , audit trails, quota and cost management.
Streamline compliance with a comprehensive portfolio spanning 60 certifications including FedRAMP High and DISA IL5.