Amazon SageMaker DebuggerAmazon
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Amazon SageMaker JumpStartAmazon
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Related Products
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About
Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values.
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About
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Businesses seeking a tool to optimize ML models with real-time monitoring of training metrics and system resources
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Audience
Companies looking for a Machine learning hub solution that has built-in algorithms, foundation models
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAmazon
Founded: 1994
United States
aws.amazon.com/sagemaker/debugger/
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Company InformationAmazon
Founded: 2006
United States
aws.amazon.com/sagemaker/jumpstart/
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Categories |
Categories |
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Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
AWS Lambda
Amazon CloudWatch
Amazon SageMaker Studio
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
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Integrations
Amazon SageMaker
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
AWS Lambda
Amazon CloudWatch
Amazon SageMaker Studio
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
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