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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Mphasis DeepInsights Card Fraud Analyzer
By:
Latest Version:
2.7
Deep Learning powered classification solution generates insights from highly skewed data with relevant class (e.g. fraud cases) < 1% of data
Product Overview
DeepInsights Card Fraud Analyzer is a Deep-Learning powered classification solution that provides valuable insights from any data that is highly skewed with relevant class (e.g. fraudulent transactions) being represented by less than 1% of data. The solution works with numerical data and provides best results when input data includes customer’s demographics and transaction history along with current transaction.
Key Data
Version
By
Type
Model Package
Highlights
Model is first trained and validated on the user provided training data. Trained model can then be deployed in production.
The solution works with numerical data and provides best results when input data includes customer’s demographics and transaction history along with current transaction. The analysis can be used for solving multiple problems that require working with highly skewed data and can be extended for multi-class classification problems
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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$10.00/hr
running on ml.m5.large
Model Batch Transform$20.00/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 | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large Vendor Recommended | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input:
The algorithm works with numerical data only.
- Mandatory fields: ‘Time’, ‘Amount’, ‘Class’
- Class takes values 0 and 1. 1 for fraudulent transactions and 0 for non-fraudulent transactions
- Time takes integer value and is time of transaction. Time of 1st transaction is 0. Time for other transactions is time elapsed in seconds between 1st transaction and the said transaction
- Amount is amount of transaction
- Input file size should not exceed 5 mb.
- Supported content types : 'text/csv'
Output:
- The algorithm returns original data along with 'pred_y' column as predicted class.
- Supported content types: 'text/csv'
Invoking endpoint:
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 out.csv
Substitute the following parameters:
"endpoint-name"
- name of the inference endpoint where the model is deployedinput.csv
- input image to do the inference ontext/csv
- MIME type of the given input file (above)out.csv
- filename where the inference results are written to
Resources:
Additional Resources
End User License Agreement
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Support Information
Mphasis DeepInsights Card Fraud Analyzer
For any assistance reach out to us at:
AWS Infrastructure
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