Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a US consumer uses Uber regularly
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer uses Uber regularly. Based on a set of basic demographics, the model identifies individuals likely to use Uber. 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 an Uber user. Propensity scores can be used to make your marketing spend more effective by focusing on consumers with a high propensity. Key Metrics: Accuracy=.89 AUC=.78 Lift over random=3.37
- 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.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge 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.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge 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 |
Dimensions summary
This product bills by usage. You pay for the machine learning model that predicts how likely consumers are to use Uber regularly. Most dimensions are batch inference charges, priced per host hour. Each one maps to a specific compute instance size across general-purpose, compute-optimized, and GPU instance families. Larger or GPU-backed instances handle heavier batch scoring workloads. You choose the instance that fits your job. One separate dimension bills per inference request instead of by host hour. Your total cost scales with which instances you run, how long they run, or how many requests you make.
Top-of-mind questions for buyers
You pay to run a predictive model that scores how likely U.S. consumers are to use Uber regularly. The model draws on more than two decades of monthly survey data on consumer intent and behavior. You are billed for the compute time or requests used to generate those scores.
Batch host-hour dimensions meter the running time of a chosen compute instance during batch scoring. You pay for each hour the instance runs. The per-request dimension meters individual inference calls instead. Batch host-hour suits scoring large datasets at once; per-request suits smaller, on-demand scoring jobs.
Host-hour dimensions bill only while the instance runs your batch job. Once the job completes and the instance shuts down, host-hour charges stop. The per-request dimension bills only when you submit inference requests. Idle time with no active job or requests generates no software charges.
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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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