Sold by: ElectrifAi LLCÂ
Deployed on AWS
Predict call volumes for events and reduce number of calls to call center by identifying customers likely to call and the reasons.
Overview
Expand customer share of wallet by personalized hotel and car recommendations determined in real-time and served through multiple booking channels. The model also predicts call volumes for events and reduces calls to call center by identifying customers likely to call and their reasons for calling. Technical highlights include ensemble modeling techniques, clustering, and reinforcement learning to understand reasons for calling and optimal treatments. Combines various fundamental reason codes to develop a holistic reason for calling.
To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data.
Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: CCRDT-PT-TLC-AWS-001
Highlights
- Expand customer share of wallet by stimulating customers to attach flight (paid for seating) and non-flight ancillary products (hotels and cars) to every flight booking.
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.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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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-2021-3177Â ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file with 5 comma separated csvs. Reference file: sample.zip
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-call-center-reduction/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-call-center-reduction/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 |
|---|---|---|---|
Input sample.zip contains 5 csv files: | scoring_date.csv (required), accounts.csv (required), profiles.csv (required), services.csv (required), transactions.csv (required), | Type: FreeText | Yes |
Resources
Vendor resources
Support
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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