Sold by: ElectrifAi LLCÂ
Deployed on AWS
Generate optimal movie theater show schedules to maximize profit while satisfying customers.
Overview
Generate optimal show schedules for movie theaters by finding the combination of shows to maximize profit while satisfying customers. Previous model usage generated optimized show time schedules that were better aligned with demand peaks, better screen utilization and optimized open/close times led to 20% more shows, more variety scheduled offered 14% more films, and a data led approach resulted in distributor negotiation.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: SCHOP-PS-ENT-AWS-001
Highlights
- Generate optimal show schedules for movie theaters by finding the combination of shows to maximize profit while satisfying customers.
- Technical highlights include an advanced algorithm and techniques to drive optimized schedules for each theater. By using data input of auditorium info, show schedule, movie attributes, distributors, screen info, ticket/retail transactions, weather, schedule constraints, etc., the model outputs the optimal show schedule.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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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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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 6 comma separated (csv) files. Reference file: sample.zip Transaction.csv (REQUIRED) Parameter.csv (REQUIRED) Movie.csv (REQUIRED) Cinema.csv (REQUIRED) Schedule.csv (REQUIRED) Holiday.csv (REQUIRED)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-schedule-optimization/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-schedule-optimization/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 6 comma separated (csv) files. Reference file: sample.zip | Transaction.csv (REQUIRED)
Parameter.csv (REQUIRED)
Movie.csv (REQUIRED)
Cinema.csv (REQUIRED)
Schedule.csv (REQUIRED)
Holiday.csv (REQUIRED) | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
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