Sold by: Mphasis
Deployed on AWS
A hybrid quantum computing-based approach for optimal feature selection in machine learning.
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.
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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Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00/host/hour |
ml.m5.2xlarge Training Recommended | Algorithm training on the ml.m5.2xlarge instance type | $10.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.00/host/hour |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.00/host/hour |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 1.1
Additional details
Inputs
- 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/gzip, text/plain
https://github.com/Mphasis-ML-Marketplace/Quantum-Feature-Selection-for-ML/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Quantum-Feature-Selection-for-ML/tree/main/input
Resources
Vendor resources
Support
Vendor support
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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