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.
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Quantum Emulator:Vehicle Damage Analysis
By:
Latest Version:
2.1
Quantum Emulator based vehicle damage classifier is a Hybrid QML image classifier designed to detect damaged vehicle images.
Product Overview
This is a Hybrid Quantum Machine Learning solution which detects damaged vehicle images. The algorithm runs on a Quantum Computing emulator and is built on cutting-edge quantum mechanics theory of machine learning embedded with classical pretrained deep learning model. The algorithms used in this solution inherits deep quantum circuit layers with trained parameters dedicated for vehicle image classification.
Key Data
Version
By
Type
Model Package
Highlights
Businesses such as car insurance servicing face time consuming task to visit the vehicle to judge the damage resulting in loss of time, and thus delay in insurance payment for customers. To identify the root cause causing the damage, it is important to classify the image as damaged. This solution helps users by analyzing images of vehicles and predicting if they are damaged or not.
Quantum Machine Learning is a computational learning methodology and leveraging quantum capabilities enhances the training of input data, thereby resulting in the algorithm learning more complex images. Damaged vehicle classifier utilizes the power of classical computing as well quantum computing by constructing a hybrid model to classify damaged vehicle images.
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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.
Contact us to request contract pricing for this product.
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$20.00/hr
running on ml.m5.large
Model Batch Transform$40.00/hr
running on ml.m5.large
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$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
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.m4.4xlarge | $20.00 | |
ml.m5.4xlarge | $20.00 | |
ml.m5d.24xlarge | $20.00 | |
ml.c5d.large | $20.00 | |
ml.m4.16xlarge | $20.00 | |
ml.m5.2xlarge | $20.00 | |
ml.p3.16xlarge | $20.00 | |
ml.c5d.4xlarge | $20.00 | |
ml.m4.2xlarge | $20.00 | |
ml.c5.2xlarge | $20.00 | |
ml.c5d.9xlarge | $20.00 | |
ml.p3.2xlarge | $20.00 | |
ml.c4.2xlarge | $20.00 | |
ml.m4.10xlarge | $20.00 | |
ml.c4.xlarge | $20.00 | |
ml.m5.24xlarge | $20.00 | |
ml.m5d.xlarge | $20.00 | |
ml.m5d.large | $20.00 | |
ml.c5.xlarge | $20.00 | |
ml.p2.xlarge | $20.00 | |
ml.m5.12xlarge | $20.00 | |
ml.m5d.4xlarge | $20.00 | |
ml.p2.16xlarge | $20.00 | |
ml.c4.4xlarge | $20.00 | |
ml.c5.large | $20.00 | |
ml.m5.xlarge | $20.00 | |
ml.c5.9xlarge | $20.00 | |
ml.m4.xlarge | $20.00 | |
ml.c5.4xlarge | $20.00 | |
ml.m5d.2xlarge | $20.00 | |
ml.c5d.xlarge | $20.00 | |
ml.p3.8xlarge | $20.00 | |
ml.m5d.12xlarge | $20.00 | |
ml.c4.large | $20.00 | |
ml.m5.large Vendor Recommended | $20.00 | |
ml.c4.8xlarge | $20.00 | |
ml.p2.8xlarge | $20.00 | |
ml.t2.xlarge | $20.00 | |
ml.c5.18xlarge | $20.00 | |
ml.c5d.18xlarge | $20.00 | |
ml.t2.large | $20.00 | |
ml.t2.medium | $20.00 | |
ml.t2.2xlarge | $20.00 | |
ml.c5d.2xlarge | $20.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input:
- Supported content type:
application/zip
- Input zip folder should not contain more than 50 images.
- Image size should not exceed 300 KB
- 90 percent of the image portion must contain the damaged/ not damaged vehicle
- Less noisy images are expected for better results, where noise constitutes human hands, vehicles etc.
- One image must contain only 1 shipment (either damaged or not damaged)
Output:
Instructions for score interpretation:
- Content type:
text/csv
- Two columns: 'filename' and 'prediction'
- Column 'filename' contains files' name along with prediction class present in the 'prediction' column in the same row.
- The prediction classes '0' and '1' indicate Damaged and Not_Damaged respectively.
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'application/zip' --region us-east-2 output.csv
Substitute the following parameters:
"model-name"
- name of the inference endpoint where the model is deployedfile_name
- input zip file nameapplication/zip
- type of the given inputoutput.csv
- filename where the inference results are written to
Resources:
Additional Resources
End User License Agreement
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Support Information
Quantum Emulator:Vehicle Damage Analysis
For any assistance reach out to us at:
AWS Infrastructure
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