Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a US consumer enjoys to Snow Ski
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer is a Snow Skier. Based on a set of basic demographics, the model identifies individuals likely to Snow Ski as a leisure time activity. The model was trained with data from Prosper's large Media Behaviors & Influence (MBI) study (N=16,619).
Highlights
- Enhances digital and offline targeting by identifying individuals likely to be a Snow Skier. Propensity scores can be used to make your marketing spend more effective by focusing on consumers with a high propensity. Key Metrics: Accuracy=.90 AUC=.70 Lift over random=1.94
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample US consumer survey data (N=16,619).
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 |
|---|---|---|
ml.m4.2xlarge Inference (Batch) Recommended | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $500.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $500.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $500.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $500.00/host/hour |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.10xlarge Inference (Batch) | Model inference on the ml.m4.10xlarge instance type, batch mode | $500.00/host/hour |
Dimensions summary
This product bills by usage on Amazon SageMaker. You pay for the model that predicts consumer propensity for snow skiing. Most dimensions charge per host hour for batch inference, priced by the machine type you run. General-purpose (m4, m5), compute-optimized (c4, c5), and GPU instances (p2, p3) span a range of sizes. Larger instance sizes carry higher hourly rates, so cost scales with the compute you choose. A separate dimension bills per inference request, letting you pay by prediction volume instead of runtime.
Top-of-mind questions for buyers
One host hour is one running hour of the machine type you select for batch inference. Each instance type (m4, m5, c4, c5, p2, p3) meters its own hours. You pay for the time the model runs a batch job, billed by the instance size you choose.
These are separate billing paths. Batch inference dimensions bill per host hour of runtime. The inference.count dimension bills per prediction request instead. You use whichever mode fits your job. Runtime-based hours suit large batch scoring; request-based charges suit lower-volume, on-demand predictions.
Host hour charges apply only while the instance runs a batch inference job. A fully stopped instance does not accrue host hour charges. Underlying AWS storage or infrastructure fees may still apply, but the model software meters running time only.
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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
Input
Content type: text/csv Input specification: gender,age_range,household_income_range
Substitute the integer codes as defined at https://github.com/goprosper/prosper-sagemaker-basic/blob/master/using_prosper_model_package_basic.ipynb for gender, age_range and household_income_range.
Sample intput: 0,1,14
Output
Content type: text/csv
The output is a single decimal number between 0 and 1 that represents the probability that the person is fashion conscious.
Sample output: 0.7214754223823547
Invoking endpoint
AWS CLI Command
You can invoke endpoint using AWS CLI:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body "input" --content-type text/csv out.txtSubstitute the following parameters:
- "endpoint-name" - name of the inference endpoint where the model is deployed
- "input" - the comma-delimited input string as defined above
- out.txt - filename where the inference results are written
Python
Real-time inference snippet (comprehensive real-time inference and batch transform examples using Python can be found in the sample notebook):
runtime = boto3.Session().client(service_name='runtime.sagemaker') input = "0,1,14" response = runtime.invoke_endpoint(EndpointName='endpoint-name', ContentType='text/csv', Body=input) results = response['Body'].read().decode('utf-8')Resources
- 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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