Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a US consumer is fashion conscious.
Overview
Prosper Insights & Analytics' Fashion Conscious propensity model predicts the probability that a U.S. adult consumer is fashion conscious. Based on a set of basic demographics, the model identifies individuals for whom the newest fashion trends and styles are important. The model was trained with data from Prosper's large database of U.S. adult consumer intentions and actions.
Highlights
- Enhances digital and offline targeting by identifying individuals for whom the newest fashion trends and styles are important. Propensity scores can be used to make your marketing spend more effective by focusing on consumers with a high propensity. Key Metrics: Accuracy=.80 AUC=.72 Lift over random= 0.93
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample US 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 |
|---|---|---|
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 based on usage, with no upfront commitment. Most dimensions bill by host hour for batch inference, meaning you run this predictive model on a chosen compute instance and pay for the hours it runs. The instance types span general-purpose, compute-optimized, and GPU-accelerated families in several sizes. Larger or GPU instances process heavier workloads faster but cost more per hour. You select the instance that fits your batch job. One separate dimension bills per inference request, charging by the number of predictions generated instead of runtime hours.
Top-of-mind questions for buyers
One host hour is one hour that a chosen compute instance runs your batch inference job. You pay for each hour the instance stays active while scoring your data. Charges accrue only while the job runs. When the batch job finishes and the instance stops, host-hour charges end.
The two metrics bill independently. Batch inference dimensions charge by host hours the instance runs. The separate request dimension charges by the number of predictions generated. You pay only for the mode you use for a given job, not both at once for the same work.
The per-request dimension meters each prediction produced, so cost tracks prediction volume. The batch host-hour dimensions meter running time on a chosen instance, so cost tracks how long the job runs. Per-request suits steady, on-demand scoring. Host-hour suits large batches processed in one run.
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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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