Sold by: Mphasis
Deployed on AWS
Deep Learning powered classification solution generates insights from highly skewed data with relevant class (e.g. fraud cases) < 1% of data
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.
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
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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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 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 deployed
- input.csv - input image to do the inference on
- text/csv - MIME type of the given input file (above)
- out.csv - filename where the inference results are written to
Resources:
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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