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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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Flight Delay Prediction

Latest Version:
1.1
This solution predicts flight delays based on factors such as route, airport congestion, airline efficiency etc. using a trainable ML model

    Product Overview

    Flight delays could cause airlines to incur financial losses in the form of accommodation expenses for the delayed passengers as well as penalties, fines, and operational costs for aircraft labor retention at airports. Furthermore, continual unexpected delays could cause the airline to lose their customers. This solution predicts whether a flight would be delayed at the origin airport and by how many minutes. It utilizes latent factors such as flight route, airport congestion, airline efficiency and temporal features derived from U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics data on flight on-time performance for large air carriers. The solution uses tree-based models to capture and predict on-time behavior of commercial flights.

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • The solution uses latent airport and airline specific operational features obtained from standardized U.S. DoT flight on-time performance data. The solution can be trained on client data to capture and predict client specific operational patterns.

    • Delay in a flight causes subsequent flights to be delayed causing the aircraft and crew schedules to be negatively impacted. Being able to predict the delay allows for better operational planning at the destination airport based on expected flight delay at origin. It also allows for better customer communication in providing flight recommendations for multi-leg journeys and avoid potential over-scheduling.

    • Mphasis HyperGraf is an Omni-channel customer 360 analytics solution. Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP 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

    Algorithm Training$10/hr

    running on ml.m5.large

    Model Realtime Inference$10.00/hr

    running on ml.m5.large

    Model Batch Transform$20.00/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 Algorithm Training$0.115/host/hr

    running on ml.m5.large

    SageMaker Realtime Inference$0.115/host/hr

    running on ml.m5.large

    SageMaker Batch Transform$0.115/host/hr

    running on ml.m5.large

    Algorithm Training

    For algorithm training 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
    Algorithm/hr
    ml.m4.4xlarge
    $10.00
    ml.m5.4xlarge
    $10.00
    ml.m4.16xlarge
    $10.00
    ml.m5.2xlarge
    $10.00
    ml.p3.16xlarge
    $10.00
    ml.m4.2xlarge
    $10.00
    ml.c5.2xlarge
    $10.00
    ml.p3.2xlarge
    $10.00
    ml.c4.2xlarge
    $10.00
    ml.m4.10xlarge
    $10.00
    ml.c4.xlarge
    $10.00
    ml.m5.24xlarge
    $10.00
    ml.c5.xlarge
    $10.00
    ml.p2.xlarge
    $10.00
    ml.m5.12xlarge
    $10.00
    ml.p2.16xlarge
    $10.00
    ml.c4.4xlarge
    $10.00
    ml.m5.xlarge
    $10.00
    ml.c5.9xlarge
    $10.00
    ml.m4.xlarge
    $10.00
    ml.c5.4xlarge
    $10.00
    ml.p3.8xlarge
    $10.00
    ml.m5.large
    Vendor Recommended
    $10.00
    ml.c4.8xlarge
    $10.00
    ml.p2.8xlarge
    $10.00
    ml.c5.18xlarge
    $10.00

    Usage Information

    Training

    The algorithm requires data in the format as described for best results:

    • The inputs must be provided as a CSV file with mandatory information in columns.
    • The input data files must contain all columns specified in input data description; other columns will be ignored.
    • The input data files must contain the column 'DEPARTURE_DELAY' with Total Delay on Departure in minutes.
    • Training Data File name should be train.csv
    • Test Data File name should be test.csv
    • For detailed instructions, please refer sample notebook and algorithm input details

    Channel specification

    Fields marked with * are required

    training

    *
    Input modes: File
    Content types: application/zip, text/plain, application/json, text/csv
    Compression types: None

    test

    Input modes: File
    Content types: text/csv, text/plain, application/json, application/zip
    Compression types: None

    Model input and output details

    Input

    Summary

    Input should be a .zip file containing a single csv with all columns in the train/test file except for 'DEPARTURE_DELAY' column.

    Input MIME type
    application/zip
    Sample input data

    Output

    Summary

    Output would be a .zip file containing a single csv (‘input_filename_results.csv’) The csv file contains the details provided by the user along with the classifier and regressor predictions.

    Output MIME type
    application/json
    Sample output data

    Additional 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

    Flight Delay Prediction

    For any assistance reach out to us at:

    AWS Infrastructure

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    Refund Policy

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