Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a US consumer has OCD.
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer has a specific health condition. Based on a set of basic demographics, the model identifies individuals who are likely to have the health condition. 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 our likely to have a OCD. Propensity scores can be used to make your marketing spend more effective by focusing on consumers with a high propensity. Key Metrics: Accuracy=.79 AUC=.67 Lift over random=1.12
- 100% Privacy Compliant Models. No PII Used. HIPAA compliant.
- 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
You pay by usage for running this propensity model through a managed machine learning service. Most dimensions charge by the hour (HostHrs) for batch inference, where the model scores data in bulk. Your rate depends on the compute instance you pick. General-purpose (m4, m5), compute-optimized (c4, c5), and GPU-based (p2, p3) families are available in sizes from large up to 24xlarge. Larger instances carry higher hourly rates for more processing power. One separate dimension bills per inference request (Requests) instead of by the hour, giving you a per-call option.
Top-of-mind questions for buyers
One HostHrs equals one hour of a single chosen instance running batch inference. Batch mode scores a dataset in bulk rather than in real time. You are billed for the hours the instance runs the job. The hourly rate changes with the instance family and size you select.
The two bill independently. The HostHrs dimensions meter running time of the instance you pick for batch jobs. The per-request dimension (Requests) counts individual inference calls instead. You pay for whichever path you run. For steady bulk scoring, instance-hours drive cost; for per-call scoring, request volume drives it.
The HostHrs dimensions meter the hours an instance runs the batch job. When no job runs and the instance is not provisioned, software charges stop. Batch mode spins resources up for the job, then releases them, so you are billed for active processing time rather than idle standby.
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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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