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.
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DeepInsights Branch Location Predictor
By:
Latest Version:
2.3
This solution performs relative bench marking of Metropolitan Statistical Areas (MSA) and predicts their attractiveness
Product 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.
Key Data
Version
By
Type
Model Package
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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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$8.00/hr
running on ml.m5.large
Model Batch Transform$16.00/hr
running on ml.m5.large
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$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
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.m4.4xlarge | $8.00 | |
ml.m5.4xlarge | $8.00 | |
ml.m4.16xlarge | $8.00 | |
ml.m5.2xlarge | $8.00 | |
ml.p3.16xlarge | $8.00 | |
ml.m4.2xlarge | $8.00 | |
ml.c5.2xlarge | $8.00 | |
ml.p3.2xlarge | $8.00 | |
ml.c4.2xlarge | $8.00 | |
ml.m4.10xlarge | $8.00 | |
ml.c4.xlarge | $8.00 | |
ml.m5.24xlarge | $8.00 | |
ml.c5.xlarge | $8.00 | |
ml.p2.xlarge | $8.00 | |
ml.m5.12xlarge | $8.00 | |
ml.p2.16xlarge | $8.00 | |
ml.c4.4xlarge | $8.00 | |
ml.m5.xlarge | $8.00 | |
ml.c5.9xlarge | $8.00 | |
ml.m4.xlarge | $8.00 | |
ml.c5.4xlarge | $8.00 | |
ml.p3.8xlarge | $8.00 | |
ml.m5.large Vendor Recommended | $8.00 | |
ml.c4.8xlarge | $8.00 | |
ml.p2.8xlarge | $8.00 | |
ml.c5.18xlarge | $8.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
1) The input file should be in the "csv" form with 'utf-8' encoding.
2) The input can have a maximum of 1000 records
3) Supported content types: text/csv
4) Input should contain parameters provided by the HMDA (Home Mortgage Disclosure Act) dataset (Largest open-source mortgage data set in the US)
5) Please refer the following link to get the list of input parameters and the sample input file: https://tinyurl.com/tga5hsd
Output
1) Sample interim output file from the model will contain the following fields: | MSA_Name | MSAID | Supply-Demand_fullfillment | | ABILENE, TX | 10180 | 0.93913999 |
2) 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.
3) Supported Content type: text/csv
4) Sample output : https://tinyurl.com/wcyx88h
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.csv
Substitute the following parameters:
endpoint-name
- name of the inference endpoint where the model is deployedinput.csv
- input filetext/csv
- MIME type of the given input file (above)result.csv
- filename where the inference results are written to.
Resources
Additional Resources
End User License Agreement
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Support Information
DeepInsights Branch Location Predictor
For any assistance reach out to us at:
AWS Infrastructure
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