Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a US consumer plays Team Sports
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer plays Team Sports. Based on a set of basic demographics, the model identifies individuals likely to play Team Sports 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 US individuals likely to be Play Team Sports. Propensity scores can be used to make your marketing spend more effective by focusing on consumers with a high propensity. Key Metrics: Accuracy=.82 AUC=.77 Lift over random=1.54
- 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 runs a propensity model that predicts which consumers are likely to play team sports. You pay based on usage, with no upfront commitment. Most dimensions bill by the hour for running batch inference on a specific machine learning instance type. You choose the instance size that fits your workload, from general-purpose types to compute-optimized and GPU-based options. Larger instances cost more per hour because they provide more processing power. One separate dimension bills by request count instead of by host hour. You select the option that matches how you want to run and pay for scoring.
Top-of-mind questions for buyers
One host hour is one hour that a chosen machine learning instance runs a batch scoring job. You pick the instance type, such as general-purpose, compute-optimized, or GPU-based. Billing counts the running time of that instance. The clock covers the full duration the instance processes your batch, not per record scored.
The two meters work differently. Host hour dimensions bill by how long a selected instance runs a batch job. The request dimension bills by the count of inference requests you submit. You choose one approach per workload. Batch host hours suit large scheduled scoring runs; request pricing suits on-demand scoring by volume.
Host hour charges apply while the selected instance runs your batch job. When no batch job runs, no host hour charges accrue for the software. Charges begin when the instance starts processing and stop when the job ends. Underlying AWS infrastructure fees may follow separate AWS terms.
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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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