Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a China consumer enjoys a specific leisure time activity.
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a China adult consumer enjoys a specific leisure time activity. Based on a set of basic demographics, the model identifies individuals who are likely to participate in the activity. The model was trained with data from Prosper's large China Quarterly survey.
Highlights
- Enhances digital and offline targeting by identifying individuals likely to enjoys a specific leisure time activity.
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample consumer survey data.
Details
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Delivery method
Latest version
Deployed on AWS
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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/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $500.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $1.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $500.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $500.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $500.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $500.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $500.00 |
Dimensions summary
You pay by the host hour for running this predictive model on Amazon SageMaker. Pricing is organized around two choices. First, you pick an inference mode: batch, which processes data in scheduled groups, or real-time, which scores requests as they arrive. Second, you pick a SageMaker compute instance type, which sets the processing power behind each host hour. Options range from smaller general-purpose instances to larger memory, compute, and GPU-based instances. Each instance and mode combination carries its own hourly rate. Larger instances cost more per hour. You are billed only for the hours you use.
Top-of-mind questions for buyers
One host hour is one hour that a chosen SageMaker instance runs the model. Charges accrue only while the instance is active and processing. When you stop the instance, software charges stop. You pay for actual running hours, not idle or unused capacity.
Batch mode processes grouped data on a schedule and runs only while the job executes. Real-time mode keeps an instance running to score requests as they arrive. Batch suits large periodic scoring. Real-time suits on-demand scoring. Each mode is billed per host hour at its own rate.
The model draws on Prosper's monthly U.S. consumer survey data, which captures spending intentions and shopping behavior. It produces a propensity score estimating how likely a consumer is to be a China leisure traveler. You can use these scores for targeting, personalization, and demand forecasting.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
Initial release.
Additional details
Inputs
- Summary
For complete usage instructions, sample data, and sample code, go to: https://github.com/goprosper/prosper-sagemaker-china-full.git
- Input MIME type
- text/csv
See Input Summary
See Input Summary
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