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 is usage-based, so you are billed only for the host hours you consume. Each dimension pairs a specific machine learning instance type with one of two inference modes: batch, which scores data in scheduled groups, or real-time, which scores requests as they arrive. Instance types range across general-purpose (m4, m5), compute-optimized (c4, c5), and GPU-accelerated (p2, p3) families in sizes from large to 24xlarge. Larger instances add processing capacity. You select the instance size and mode that fit your workload.
Top-of-mind questions for buyers
One host hour is one hour that a chosen machine learning instance runs your model. You pay per instance, per hour of run time. Each instance type carries its own rate. Sizes such as large, xlarge, and 24xlarge define the compute capacity assigned during that hour.
Charges accrue for the host hours the instance is running. Batch mode runs the instance only while scoring scheduled data groups, so metering stops when the job ends. Real-time mode keeps an instance available for incoming requests, so it meters while the endpoint stays live.
Batch mode scores data in scheduled groups, so you run instances only during those jobs and pay for that window. Real-time mode scores requests as they arrive and keeps an endpoint running, so host hours accrue for the whole period the endpoint stays active.
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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
Resources
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