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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

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

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    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.

    • Need customized image analytics solutions? Get in touch!

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    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 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 deployed
    • file_name - input zip file name
    • application/zip - type of the given input
    • output.csv - filename where the inference results are written to

    Resources:

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Quantum Emulator:Vehicle Damage Analysis

    For any assistance reach out to us at:

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    Refund Policy

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