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
By:
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
Version
By
Type
Algorithm
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.
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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
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 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 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/zipSample 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/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Flight Delay Prediction
For any assistance reach out to us at:
AWS Infrastructure
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