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 hour for the compute instance that runs this predictive model. Pricing follows two usage modes: batch inference for scoring data in bulk, and real-time inference for on-demand scoring. Within each mode, you choose from a range of instance types across general-purpose, compute-optimized, and GPU-accelerated families. Larger instance sizes carry higher hourly rates, so cost scales with the processing power you select. You are billed only for the hours each instance runs. Match the instance type and mode to your workload to control what you spend.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a single model instance runs. You are charged for each hour an instance stays active. The rate depends on the instance type you pick. Partial hours and hours where the instance is stopped follow standard hosting metering, so cost tracks running time.
Batch inference scores data in bulk during scheduled runs, so you pay only for the hours those jobs run. Real-time inference keeps an instance running to score requests on demand, so charges accrue while it stays live. Batch suits periodic scoring; real-time suits continuous, on-demand scoring.
Instance size drives cost most. Larger instance types in the general-purpose, compute-optimized, and GPU-accelerated families carry higher hourly rates. Your total is the hourly rate multiplied by hours run, per instance. Choosing a size matched to your workload controls what you spend within either mode.
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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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AWS infrastructure support
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