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.
Auto Deep Learning for Tabular Data
By:
Latest Version:
2.0
This solution automatically identifies and trains the best performing deep learning model for tabular data.
Product Overview
This solution will evaluate between several deep learning models of various architectures on the user provided data. It will identify the best performing deep learning model architecture on the basis of validation metric for tabular data. This will reduce the time and effort for the model building task for a data scientist. This solution automates several of deep learning tasks in data science. This can be used for both regression and classification.
Key Data
Version
By
Type
Algorithm
Highlights
This solution will help identify the best deep learning model architecture for the tabular data set and help improve the turn-around time for any tabular data processing as well as save time of a data scientist.
This solution can be used for tabular data in various domains like banking, insurance, retail, legal and ecommerce.
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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.
Contact us to request contract pricing for this product.
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
Algorithm Training$10/hr
running on ml.m5.4xlarge
Model Realtime Inference$10.00/hr
running on ml.m5.4xlarge
Model Batch Transform$20.00/hr
running on ml.m5.2xlarge
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 Algorithm Training$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Realtime Inference$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
Algorithm Training
For algorithm training 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 | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge Vendor Recommended | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Training
This algorithm takes a zip file as an input. This zip file to be uploaded for training the model should contain only two files which are: train.csv and dict.json
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: text/csv, application/zip
Compression types: None, Gzip
Model input and output details
Input
Summary
This zip file to be uploaded for testing the trained model should contain the test.csv file. There should be exactly tow columns in this file. The first column should contain the text the second column the corresponding labels.
Input MIME type
text/csv, text/plain, application/zipSample input data
Output
Summary
• In training mode, the output will be the trained model • In test/evaluation mode, the output will be the test accuracy
Output MIME type
text/plain, application/zipSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Auto Deep Learning for Tabular Data
For any assistance reach out to us at:
AWS Infrastructure
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Learn MoreRefund Policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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