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.
Passenger Traffic Forecasting
By:
Latest Version:
3.2
This solution provides 30 weeks of forecast of passengers who are expected to travel using historical weekly passenger traffic data.
Product Overview
Passenger Traffic Forecasting generates 30 weeks of forward forecast of passengers using historical data. This solution will help businesses such as airlines, railways, bus and ferry operators to better assess the number of incoming passengers and provide them a better travel experience. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
Key Data
Version
By
Type
Model Package
Highlights
This solution will take in weekly data as input and provide 30 weeks future forecast. Automatic model selection will automatically identify the set of optimal algorithms and combine their results using ensemble learning to provide the results.
Mphasis Time Series Passengers Forecasting can be applied in Passengers Prediction.
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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$5.00/hr
running on ml.m5.large
Model Batch Transform$10.00/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 | $5.00 | |
ml.m5.4xlarge | $5.00 | |
ml.m4.16xlarge | $5.00 | |
ml.m5.2xlarge | $5.00 | |
ml.p3.16xlarge | $5.00 | |
ml.m4.2xlarge | $5.00 | |
ml.c5.2xlarge | $5.00 | |
ml.p3.2xlarge | $5.00 | |
ml.c4.2xlarge | $5.00 | |
ml.m4.10xlarge | $5.00 | |
ml.c4.xlarge | $5.00 | |
ml.m5.24xlarge | $5.00 | |
ml.c5.xlarge | $5.00 | |
ml.p2.xlarge | $5.00 | |
ml.m5.12xlarge | $5.00 | |
ml.p2.16xlarge | $5.00 | |
ml.c4.4xlarge | $5.00 | |
ml.m5.xlarge | $5.00 | |
ml.c5.9xlarge | $5.00 | |
ml.m4.xlarge | $5.00 | |
ml.c5.4xlarge | $5.00 | |
ml.p3.8xlarge | $5.00 | |
ml.m5.large Vendor Recommended | $5.00 | |
ml.c4.8xlarge | $5.00 | |
ml.p2.8xlarge | $5.00 | |
ml.c5.18xlarge | $5.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
• Supported content types: text/csv
• Sample input file: (https://tinyurl.com/y96ywse6 )
Output
• Content type: text/csv
• Sample output file:(https://tinyurl.com/yd7jb9en )
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 'text/csv' --region us-east-2 result.csv
Substitute the following parameters:
"model-name"
- name of the inference endpoint where the model is deployedfile_name
- input csv nametext/csv
- MIME type of the given inputresult.csv
- filename where the inference results are written to.
Resources
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
Passenger Traffic Forecasting
For any assistance, please reach out at:
AWS Infrastructure
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Learn MoreRefund Policy
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