Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Planning Vacation Travel.
Overview
This Pandemic Propensity Model determines the probability that a US adult is Planning Vacation Travel. Lift over Random 1.43
This Pandemic Propensity model is one of a series of consumer classification models based on data from over 24,000 US adults surveyed between April to June 2020 from Prosper's US Monthly Consumer survey. Survey data was collected during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences during the National Emergency. The survey is anonymous. Zero PII. CCPA and HIPAA Compliant.
Highlights
- Enhances digital and offline targeting by identifying an individual’s probability to engage in a specific behavior. Model is based on data from over 24.000 US adults surveyed between April to July 2020 from Prosper's US Monthly Consumer survey.
- 100% Privacy Compliant Models. No PII Used.
- Lift over Random 1.43
Details
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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/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $350.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.28 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $350.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $350.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $350.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $350.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $350.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $350.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $350.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $350.00 |
Dimensions summary
You pay by the hour for the compute instance that runs the model, billed by usage. Pricing splits into two modes: Batch inference scores large data sets in scheduled runs, while Real-Time inference serves live requests. Each mode offers the same set of instance types, so you pick the mode and instance that fit your workload. Instance families vary by resources: c-series is compute-focused, m-series balances general purpose, and p-series adds GPU power. Sizes range from large through 24xlarge. Larger sizes carry higher hourly rates. You pay only for hours the instance runs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen instance runs your model inference. The meter counts wall-clock hours the instance stays active, not the number of records scored or requests served. Each instance type has its own hourly rate, so you pay for the running time of the size you select.
Charges accrue only for hours the instance runs. Real-Time instances meter while the endpoint stays up and waits for live requests, even during idle moments. Batch instances meter only during scheduled scoring runs and stop when the job finishes. Shutting down the endpoint stops the hourly software charge.
Batch mode scores large data sets in scheduled runs, so instances meter only while a job processes. Real-Time mode serves live requests through a running endpoint that meters continuously until you stop it. Batch fits periodic bulk scoring; Real-Time fits ongoing on-demand predictions. Both modes share the same instance choices.
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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
None
Additional details
Inputs
- Summary
For complete usage instructions, sample data, and sample code, go to: https://github.com/goprosper/prosper-sagemaker-basic-geo.git
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Support
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