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.
Customer Segmentation Using Quantum ML
By:
Latest Version:
1.0
Quantum computing-based solution segments the credit card customers by leveraging historical data of customers
Product 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.
Key Data
Version
By
Type
Algorithm
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.
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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$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$0.922/host/hr
running on ml.m5.4xlarge
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 Vendor Recommended | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $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
Supported content: zip file with file name input.zip, having folder named input which contains two csv files: input_data.csv : must contain unique value column "CUST_ID" with other data about customer. Other colums should be perprocessed (all categorical columns should be contverted to numeric value and there should not be nan value ). see given input example. User_Input.csv: contains user's credentials (API key) for acessing Dwave Leap (a quantum cloud service) and maximum number of clusters that the solution should consider
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: text/csv, application/zip, application/x-zip-compressed
Compression types: None, Gzip
Model input and output details
Input
Summary
To invoke inference container any csv file is accepted.
The input example link is for training data.
Input MIME type
text/csvSample input data
Output
Summary
Model output is 'input_data_with_clusters.csv' file. The csv file is a modified form of 'input_data.csv', containing the 'cluster' and 'cluster_center' field for each customer.
Output MIME type
text/csv, text/plain, application/zipSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Customer Segmentation Using Quantum ML
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