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.
Quantum Feature Selection for ML
By:
Latest Version:
1.1
A hybrid quantum computing-based approach for optimal feature selection in machine learning.
Product Overview
Quantum Feature Selecton is hyrbid quantum computing approach to optimize feature selection in artificial intelligence/machine learning (AI/ML) model training and prediction. This solution approaches feature selection as an optimization problem and selects the most critical variables and eliminates the redundant and irrelevant ones. The solution increases the predictive power of machine learning applications, decreases over-fitting and reduces training time.
Key Data
Version
By
Type
Algorithm
Highlights
Feature selection process is one of the main components of a feature engineering process. It increases the predictive capability and decreases computation of a predictive model by reducing the number of input variables. This solution finds the minimal-optimal subset of features by maximizing relavancy and minimizing redudancy among features. Using this crucial component of machine learning, user's can eliminate the need of classical alternative feature selection methods like recursive feature selection and incorporate this one shot solution.
The solution uses quantum hybrid solvers from D-Wave to reduce the time and space required while providing better quality results.
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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.m5.2xlarge
Model Realtime Inference$0.00/inference
running on any instance
Model Batch Transform$0.00/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$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Realtime Inference$0.461/host/hr
running on ml.m5.2xlarge
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.c5n.18xlarge | $10.00 | |
ml.g4dn.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge Vendor Recommended | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.g4dn.2xlarge | $10.00 | |
ml.c5n.xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.g4dn.12xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.g4dn.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.g4dn.16xlarge | $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.c5n.2xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.g4dn.8xlarge | $10.00 | |
ml.c5n.9xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 | |
ml.c5n.4xlarge | $10.00 |
Usage Information
Training
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, application/gzip
Compression types: None, Gzip
Model input and output details
Input
Summary
The zip file includes two files with following name and information. a. 'input.csv' : This csv file contains features as 'feature_0', 'feature_1', upto 'feature_N',along with target column as 'Class'. The feature selection algorithm selects name of these described features.
b. 'input_config.json' : This json contains algorithm configuration including dwave credentials and dataset field descriptions.Limitations for input type
Mandatory Fields:
a. 'input_config.json': dwave_sapi_token, target_variable, discrete_features, number_of_features_to_be_selected ,alpha , number_of_runs.
Input MIME type
application/zip, application/gzipSample input data
Output
Summary
The model output is a json file containing name of the selected features. The result of the algorithm can be indexed using the key "Optimal_selected_features" in output json file.
Output MIME type
application/gzip, application/zip, text/plainSample 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
Quantum Feature Selection for ML
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AWS Infrastructure
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