Sold by: MphasisÂ
Deployed on AWS
This solution performs relative bench marking of Metropolitan Statistical Areas (MSA) and predicts their attractiveness
Overview
This solution empirically measures demand fulfillment potential of a geographical region through multi-criteria decision making and provides market attractiveness scores. The output will be scores (between 0-1) for each region. Higher score signifies higher demand fulfillment in a market. This can be used to analyze geographical regions for business decisions such as new branch location setup, branch expansion, market-entry & competition analysis. Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction and predictive analytics capabilities.
Highlights
- The predictive models are trained and tested on the US largest open-source mortgage data set (Home Mortgage Disclosure Act: https://www.ffiec.gov/hmda/)
- The solution works on numerical data where the user can perform multi-criteria decision making to obtain relative performance score for each geographical region. The solution can be extended across multiple industries including retail, financial services, insurance, logistics etc.
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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/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Vendor terms and conditions
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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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
- The input file should be in the "csv" form with 'utf-8' encoding.
- The input can have a maximum of 1000 records
- Supported content types: text/csv
- Input should contain parameters provided by the HMDA (Home Mortgage Disclosure Act) dataset (Largest open-source mortgage data set in the US)
- Please refer the following link to get the list of input parameters and the sample input file: https://tinyurl.com/tga5hsdÂ
Output
-
Sample interim output file from the model will contain the following fields: | MSA_Name | MSAID | Supply-Demand_fullfillment | | ABILENE, TX | 10180 | 0.93913999 |
-
Final ouput file will contain the following columns : (please refer to the provided Jupyter notebook to generate the final output)
- MSA_Name: Name of the Metro statistical Area
- MSA_Demad_Score_below_0.6 :MSAs where Mortagage Demand is not getting addressed hence these regions are more attractive.
- MSA_Demad_Score_between_0.6_to_0.8:MSAs where Mortagage Demand is getting partially addressed.
- MSA_Demad_Score_above_0.8:MSAs where Mortagage Demand is getting addressed.
- Supported Content type: text/csv
- Sample output : https://tinyurl.com/wcyx88hÂ
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept text/csv result.csvSubstitute the following parameters:
- endpoint-name - name of the inference endpoint where the model is deployed
- input.csv - input file
- text/csv - MIME type of the given input file (above)
- result.csv - filename where the inference results are written to.
Resources
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
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