Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Prosper Propensity*: Use Uber Regularly
Latest Version:
V1
Propensity model that determines the probability that a US consumer uses Uber regularly
Product 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).
Key Data
Version
Type
Model Package
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).
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.01/inference
running on any instance
Model Batch Transform$500.00/hr
running on ml.m4.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.24/host/hr
running on ml.m4.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
Additional infrastructure cost, taxes or fees may apply.
Usage Information
Fulfillment Methods
Amazon SageMaker
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 aboveout.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
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Support Information
AWS Infrastructure
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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