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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.

Time Series Web Traffic Forecasting

By: Mphasis Latest Version: 3.6

The solution provides 30 days forecast of web traffic using historical web traffic data.

Product Overview

Web Traffic Forecasting enables enterprises to optimize web server allocation, scaling for instances, parallelise workload traffic and utilization by generating 30 days of forward forecast of web traffic data. This helps enterprises to plan their IT infrastructure strategy across the cloud and on premise scenarios. It facilitates seamless end user experience and satisfied customers. It uses ensemble ML algorithms with automatic model selection. This solution performs automated model selection to apply the right model based on the input data, thereby providing consistent and better results.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

• Supported content types: text/csv • Sample input file: Sample input

maskedsku2015-04-04F00071338Input should have1. Have an unique identifier column called 'maskedsku'. eg. maskedsku can be your shipment id.2. The date format of the columns should be: 'YYYY-MM-DD'

Output

• Content type: text/csv • Sample output file: Sample Output

maskedsku2015-04-0420211101_forecastF000713381894

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 deployed
  • file_name - input csv name
  • text/csv - MIME type of the given input
  • result.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

Time Series Web Traffic Forecasting

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

Customer Reviews