AzureML

AzureML

A service available from Microsoft that can be purchased to aid with using and running programs that require machine learning.

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Edits on 20 Feb, 2020
Golden AI"Adding location topic from lookup Redmond, WA"
Golden AI edited on 20 Feb, 2020
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Location
Redmond, Washington
Edits on 1 May, 2019
Stijn de Vries
Stijn de Vries edited on 1 May, 2019
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Article

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.

David Hao
David Hao approved a suggestion from Golden's AI on 1 May, 2019
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Article (+7/-7 characters)
Article

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.

Edits on 1 May, 2019
Emerson Spartz
Emerson Spartz approved a suggestion from Golden's AI on 1 May, 2019
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Article (+6/-6 characters)
Article

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.

Emerson Spartz
Emerson Spartz approved a suggestion from Golden's AI on 1 May, 2019
Edits made to:
Article (+6/-6 characters)
Article

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.

Jairo Niño
Jairo Niño approved a suggestion from Golden's AI on 30 Apr, 2019
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Article (+16/-16 characters)
Article

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.

Phillip Johnston
Phillip Johnston approved a suggestion from Golden's AI on 30 Apr, 2019
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Article (+10/-10 characters)
Article

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.

Andrew Donohue
Andrew Donohue approved a suggestion from Golden's AI on 30 Apr, 2019
Edits made to:
Article (+9/-9 characters)
Article

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.

Claire McCarthy
Claire McCarthy edited on 30 Apr, 2019
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Infobox (+4/-1 properties)
Article (+1192 characters)
Article

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.

Infobox
Edits on 15 Apr, 2019
Jessica Karpinski
Jessica Karpinski edited on 15 Apr, 2019
Edits made to:
Infobox (+4 properties)
Description (+127 characters)
Categories (+1 topics)
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AzureML

A service available from Microsoft that can be purchased to aid with using and running programs that require machine learning.

Infobox
Industry
Machine learning
Categories
Jessica Karpinski"Initial topic creation"
Jessica Karpinski created this topic on 15 Apr, 2019
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 AzureML

A service available from Microsoft that can be purchased to aid with using and running programs that require machine learning.

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