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.
Spend Passion
By:
Latest Version:
1.0.1
Analyze low-utilization credit card users customer data to discover spend behavior and relationships between products or services.
Product Overview
Gain a more holistic view of low-utilization credit card users by analyzing user spend behavior from customer data to find clear relationships between products or services. Leverage look-alike model to identify customers truly passionate about a spend category. Third-party data resources integrated to strengthen customer profiles. This model drove a 5x increase in spend life in comparison to random campaigns and $500 million to $1 billion potential spend impact for top US and China credit card issuers. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: SPEND-PS-CCC-AWS-001
Key Data
Version
Type
Model Package
Highlights
Gain a more holistic view of low-utilization credit card users by analyzing user spend behavior and find relationships between products or services.
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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.
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$0.00/hr
running on ml.p2.16xlarge
Model Batch Transform$0.00/hr
running on ml.m5.2xlarge
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$16.56/host/hr
running on ml.p2.16xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.p2.xlarge | $0.00 | |
ml.p2.16xlarge Vendor Recommended | $0.00 | |
ml.p3.16xlarge | $0.00 |
Usage Information
Model input and output details
Input
Summary
A zip file containing 5 comma separated csv files. Reference file: sample.zip Bureau.csv (REQUIRED) PNL.csv (REQUIRED) Infobase.csv (REQUIRED) Scoring_date.csv (REQUIRED) Transaction.csv (OPTIONAL)
https://github.com/ElectrifAi/model-aws-spend-passion/blob/main/input_output_description.md
Input MIME type
multipart/form-dataSample input data
Output
Summary
a JSON list of objects contaning, with each customer's ID as the main key; for every entry, there up to 33 predictions in 33 different columns. The spend_pass_prob contained in each column will contain the probability (passion rate) a customer is passionate about the category, respectively. Reference file: sample.zip.out
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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