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, billed per host hour (HostHrs). Pricing splits into two processing modes: batch inference for scoring large datasets at once, and real-time inference for on-demand scoring. Within each mode, you choose an instance type. General-purpose (m4, m5) and compute-optimized (c4, c5) families handle standard workloads, while GPU families (p2, p3) suit heavier processing. Larger instance sizes cost more per hour than smaller ones. Your total cost depends on the mode, instance type, and hours run.
Top-of-mind questions for buyers
One host hour is one hour that a chosen instance type runs your inference job. Billing meters the running time of that instance. Each active instance accrues charges by the hour. Your total reflects the instance type you pick multiplied by the hours it runs.
Batch inference scores a full dataset in one run, then the instance stops and billing ends. Real-time inference keeps an instance running to answer requests on demand, so charges continue while it stays active. Choose batch for periodic scoring and real-time for live, on-request scoring.
GPU families (p2, p3) suit heavier processing workloads that benefit from parallel computation. General-purpose (m4, m5) and compute-optimized (c4, c5) families handle standard scoring. Each instance type bills per host hour, so your choice affects both processing speed and hourly cost.
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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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