Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Lymphoma Subtype Classification
Latest Version:
0.1
Classifies non-Hodgkin’s Lymphoma subtypes
Product Overview
Non-Hodgkin lymphoma (also known as non-Hodgkin’s lymphoma, NHL, or sometimes just lymphoma) is a cancer that starts in white blood cells called lymphocytes, which are part of the body’s immune system. This model illustrates the potential of Deep Learning - in contributing to the automation of cancer diagnosis, by proposing a method to classify the subtypes of Lymphoma. This model is not intended for medical and diagnostic purpose.
Key Data
Version
Type
Model Package
Highlights
Multi-class classifier model that classifies Lymphoma Images as Chronic Lymphocytic Leukaemia, Follicular and Mantle Cell Lymphoma
Trained using AlexNet (CNN Architecture)
Acknowledgement: Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform 2016;7:29. (http://www.andrewjanowczyk.com/use-case-7-lymphoma-sub-type-classification/ )
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.00/hr
running on ml.m4.xlarge
Model Batch Transform$0.00/hr
running on ml.m4.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.24/host/hr
running on ml.m4.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.xlarge Vendor Recommended | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Download the Jupyter notebook in "Additional Resources" section and follow readme.txt provided.
Additional Resources
End User License Agreement
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Support Information
AWS Infrastructure
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Learn MoreRefund Policy
Since you are not being charged currently for the use of this software there will be no refund of any charges.
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