Sold by: MphasisÂ
Deployed on AWS
Quantum computing-based solution segments the credit card customers by leveraging historical data of customers
Overview
The solution harnesses historical customer data for segmentation, incorporating customer IDs and various features such as purchase frequency, credit limits, and the number of purchases. It formulates the clustering problem as an optimization problem and solves it using the D-Wave's hybrid solver. Additionally, the solution calculates the optimal number of clusters based on the silhouette score. Consequently, the model produces optimal customer clusters as output, valuable for marketing purposes.
Highlights
- The solution employs novel optimization based approach for clustering. Through iterative processes, it continually refines and identifies the optimal clusters tailored to specific scenarios.
- The solution uses quantum hybrid solvers from D-Wave to reduce the time and space required while providing better quality results.
- Need customized Quantum Computing solutions? Get in touch!
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.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge 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
Vendor terms and conditions
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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 the first version.
Additional details
Inputs
- Summary
To invoke inference container any csv file is accepted.
The input example link is for training data.
- Input MIME type
- text/csv, application/zip, text/plain
https://github.com/Mphasis-ML-Marketplace/Customer-Segmentation-Using-Quantum-ML/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Customer-Segmentation-Using-Quantum-ML/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Training input data | Data must contain "CUST_ID" and other columns. Take reference from the follwing open source data: https://www.kaggle.com/datasets/arjunbhasin2013/ccdata | Default value: 0
Type: FreeText | No |
Resources
Vendor resources
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