Sold by: Mphasis
Deployed on AWS
This solution identifies the optimal sequence of resources for a widget with the objective of reducing congestion cost.
Overview
This solution identifies a set of optimal trajectories that complete all the jobs on a given widget while minimizing the congestion across all resources. It helps in reducing operational cost and turnaround time in production processes. The solution utilizes quantum computing simulator for optimization, making it scalable and robust.
Highlights
- The solution helps in reducing the overall congestion of servicing widgets by allocating optimal sequence of resources thereby reducing cost and time to repair. The solution leverages quantum computing in order to solve the problem in an optimal and faster way.
- This solution is applicable across a number of industries such as aerospace, automobile, manufacturing, logistics etc. This quantum computing based optimization is significantly faster than the conventional optimization approach.
- Need customized Quantum Computing solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 | $40.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
Vendor refund policy
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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
It is the first version of this solution
Additional details
Inputs
- Summary
- file name : Input.zip
- resource_data.csv : This file contains columns like 'path', 'machine_id' and all the resources columns which are in the system, 'path' column will provide the information of all the available paths ,resource details columns can change according to number of resources in the system.
- resources_constraint.csv : This file will contain columns like Path, and different columns for each machine id, this file provides information of all the available paths for each machine
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Service-allocation/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Service-allocation/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
path | column (must be in string) will provide the information of all the available paths for each machine | Type: FreeText | Yes |
machine_id | This column contains the id information of each machine in the system. | Type: Integer | Yes |
Resources
Vendor resources
Support
Vendor support
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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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