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.
Lung Cancer - Classifier
Latest Version:
1
Machine Learning model to classify subjects’ susceptibility to lung cancer based on demographics, clinical measures and behavioral features.
Product Overview
Lung cancer starts when cells of the lung become abnormal and begin to grow out of control. As more cancer cells develop, they form into a tumor. There are 2 main types of lung cancer as about 80% to 85% of lung cancers are non-small cell lung cancer (NSCLC) and about 10% to 15% are small cell lung cancer (SCLC). Early identification is pivotal in the success of the treatment. The model is trained on large dataset that simulates the real world data of US population and considers demographic, clinical and behavioral features for classification with at least 50% probability for a Positive Case.
Key Data
Version
Categories
Type
Model Package
Highlights
Easy-to-use model quickly determines whether a patients's observations can be used to predict the likeliness of disease.
Provides the most realistic scenario as it is trained on a large dataset capturing all possible patterns.
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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.m4.4xlarge | $0.00 | |
ml.m5.4xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 | |
ml.m5.2xlarge | $0.00 | |
ml.p3.16xlarge | $0.00 | |
ml.m4.2xlarge | $0.00 | |
ml.c5.2xlarge | $0.00 | |
ml.p3.2xlarge | $0.00 | |
ml.c4.2xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.c4.xlarge | $0.00 | |
ml.m5.24xlarge | $0.00 | |
ml.c5.xlarge | $0.00 | |
ml.p2.xlarge | $0.00 | |
ml.m5.12xlarge | $0.00 | |
ml.p2.16xlarge | $0.00 | |
ml.c4.4xlarge | $0.00 | |
ml.m5.xlarge | $0.00 | |
ml.c5.9xlarge | $0.00 | |
ml.m4.xlarge Vendor Recommended | $0.00 | |
ml.c5.4xlarge | $0.00 | |
ml.p3.8xlarge | $0.00 | |
ml.m5.large | $0.00 | |
ml.c4.8xlarge | $0.00 | |
ml.p2.8xlarge | $0.00 | |
ml.c5.18xlarge | $0.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Lung Cancer Patient Identifier. See Notebook for additional information.
Additional Resources
End User License Agreement
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Support Information
AWS Infrastructure
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