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 AI.
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
State-of-the-art fairness and model interpretability to build responsible AI solutions, with enhanced security and cost management for advanced governance and control.
Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python and R.
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 hyper parameter 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 AI solutions
Access state-of-the-art technology for fairness and machine learning model transparency. Use model interpretability for explanations about predictions to better understand model behavior. Reduce model bias by applying common fairness metrics, automatically making comparisons and using recommended mitigations.
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 optimise and accelerate inferencing across cloud and edge devices.