Sold by: ElectrifAi LLCÂ
Deployed on AWS
Score each location to identify locations with the greatest potential in terms of visits per hour.
Overview
Business chains have more than one location. This model scores each location to identify which one has the greatest potential in terms of visits per hour. The business can then determine which location should receive a larger budget to accommodate store upgrades. Using data input of historic store-specific performance, surrounding trading areas (competitors, neighborhoods, and demographics) drawn from a range of sources, the model outputs the expected visit per hour for the 3 years after the store opened.
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SKU: SITES-PS-RET-AWS-001
Highlights
- Business chains have more than one location. This model scores each location to identify which one has the greatest potential in terms of visits per hour.
Details
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Delivery method
Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
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This product is offered for free. If there are any questions, please contact us for further clarifications.
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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
Vulnerability CVE-2021-3177 (i.e. https://nvd.nist.gov/vuln/detail/CVE-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file contaning at least 4 comma separated csv files. Reference file: sample.zip site_info.csv (required) neighborhood.csv (required) competitor_info.csv (required) target_service_transaction.csv (required) other_service{N}transaction.csv (optional)
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-site-selection/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-site-selection/blob/main/sample.zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
A zip file contaning at least 4 comma separated csv files. Reference file: sample.zip | site_info.csv (required)
neighborhood.csv (required)
competitor_info.csv (required)
target_service_transaction.csv (required)
other_service(N)transaction.csv (optional) | Type: FreeText | Yes |
Resources
Vendor resources
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