Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a China consumer uses Alipay.
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a China adult consumer uses Alipay. Based on a set of basic demographics, the model identifies individuals who are likely to use Alipay. The model was trained with data from Prosper's large China Quarterly survey.
Highlights
- Enhances digital and offline targeting by identifying individuals likely to use Alipay.
- 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
This product is available free of charge. Free 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.
Dimensions summary
This product is free to use, so you pay only for the AWS compute you select. Pricing is organized by machine learning instance type and by two inference modes. Real-Time mode returns predictions on demand, while Batch mode scores large groups at once. Each instance type is billed per host hour. Options range from smaller general-purpose and compute-optimized instances to larger GPU instances. You scale cost by choosing a bigger instance or by running more hours. Pick the mode and instance size that match your prediction workload.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a single instance runs the model. Billing counts each active host per hour. If you run two instances for one hour, you are billed two host hours. The software itself is free, so charges reflect the underlying AWS compute time only.
Both modes bill per host hour on the instance type you pick. Real-Time keeps an instance running to answer prediction requests on demand, so you pay for uptime. Batch runs an instance only long enough to score a group of records at once, then stops, limiting charged hours to the job.
Charges apply per host hour while an instance runs. A stopped instance does not accrue host-hour charges. For Real-Time endpoints, the instance stays active to serve requests, so hours accrue until you stop it. Batch jobs release the instance when scoring finishes, limiting billed time.
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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
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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