Sold by: ElectrifAi LLCÂ
Deployed on AWS
Personalized hotel & car recommendations determined in real-time through multiple booking channels encourage customer spend.
Overview
Leverage customer signals to understand customer behavior to build a marketing plan to stimulate/capture demand. Recommendations for hotels and cars are personalized in real-time through multiple booking channels to encourage customers to add a flight (paid for seating) and non-flight ancillary products to every flight booked. Those real-time recommendations are fed into the booking session online or directly to the call center representatives when booking over the phone. 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: ANCPS-PS-AIR-AWS-001
Highlights
- Personalized ancillary products like hotel and car recommendations, determined in real time and served through multiple booking channels, including online and through call center. Previous campaigns have seen $6+M increase in incremental revenue from hotel bookings and car rentals. To preview our machine learning models, please Continue to Subscribe.
Details
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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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge 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 |
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 |
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This product is offered for free. If there are any questions, please contact us for further clarifications.
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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-2021-3177 ) has been resolved in version 1.0.1
Additional details
Inputs
- Summary
Input: A zip file containing 4 comma separated (csv) files. Reference file: sample.zip passenger_info.csv (REQUIRED) flight_booking.csv (REQUIRED) hotel_booking.csv (REQUIRED) vehicle_booking.csv (REQUIRED)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-ancillary-personalization/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-ancillary-personalization/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 |
|---|---|---|---|
A zip file containing 4 comma separated (csv) files. Reference file: sample.zip | passenger_info.csv (REQUIRED)
flight_booking.csv (REQUIRED)
hotel_booking.csv (REQUIRED)
vehicle_booking.csv (REQUIRED) | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
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