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.
Ancillary Personalization
By:
Latest Version:
1.0.1
Personalized hotel & car recommendations determined in real-time through multiple booking channels encourage customer spend.
Product 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
Key Data
Version
Type
Model Package
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.
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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.
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$0.00/hr
running on ml.p2.16xlarge
Model Batch Transform$0.00/hr
running on ml.m5.2xlarge
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$16.56/host/hr
running on ml.p2.16xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
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.p2.xlarge | $0.00 | |
ml.p2.16xlarge Vendor Recommended | $0.00 | |
ml.p3.16xlarge | $0.00 |
Usage Information
Model input and output details
Input
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/jsonSample input data
Output
Summary
A list of JSON objects containing the fields listed below. Reference file: sample.zip.out PASSENGER_ID: Passenger Id FLIGHT_CODE: Flight Code RECOMMEND_HOTEL_CODE: Recommended hotel code for passenger RECOMMEND_ROOM_TYPE: Recommended room type for passenger RECOMMEND_PICKUP_LOC_CODE: Recommended vehicle pickup location code for passenger RECOMMEND_VEHICLE_TYPE_CODE: Recommended vehicle type code for passenger
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
AWS Infrastructure
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