AzureML

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-sourceopen-source frameworks, such as PyTorch, TensorFlow, and scikit-learnscikit-learn.

Install the SDKSDK in your favorite Python environment, and create your workspace to store your compute resources, models, deployments, and run histories in the cloud.

Use frameworks of your choice and automated machine learning capabilities to identify suitable algorithms and hyperparameters faster. Track your experiments and easily access powerful GPUsGPUs in the cloud.

Deploy models to the cloud or at the edge and leverage hardware-accelerated models on field-programmable gate arraysfield-programmable gate arrays (FPGAs) for super-fast inferencing. When your model is in production, monitor it for performance and data drift, and retrain it as needed.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorchPyTorch, TensorFlow, and scikit-learn.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite PythonPython environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOpsDevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learningmachine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlowTensorFlow, and scikit-learn.

A service offered by MicrosoftMicrosoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.

A service offered by Microsoft that enables users to accelerate the building, training, and deployment of machine learning models. Users can use automated machine learning to identify suitable algorithms and tune hyperparameters faster. They can improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. They can seamlessly deploy to the cloud and the edge with one click, and access all these capabilities from their favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.

Install the SDK in your favorite Python environment, and create your workspace to store your compute resources, models, deployments, and run histories in the cloud.

Use frameworks of your choice and automated machine learning capabilities to identify suitable algorithms and hyperparameters faster. Track your experiments and easily access powerful GPUs in the cloud.

Deploy models to the cloud or at the edge and leverage hardware-accelerated models on field-programmable gate arrays (FPGAs) for super-fast inferencing. When your model is in production, monitor it for performance and data drift, and retrain it as needed.