Sign in
Sign in
or
Create a new account
Categories
Your Saved List Partners Sell in AWS Marketplace Amazon Web Services Home Help
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.

Cloud Storage Cost Forecasting

By: Mphasis Latest Version: 3.0

The solution provides 24 hours future forecast of the cost incurred for cloud storage using historical data.

Product Overview

Cloud storage cost forecasting helps businesses assess the cost incurred from their cloud storage based on historic data. This will help businesses get an understanding of the potential cost for their cloud resources and help them plan better to manage storage services like S3 buckets, EC2 storage, Elastic Block Store, Amazon Glacier etc. it uses ensemble ML algorithms with automatic model selection algorithms. This solution performs automated model selection to apply the right model based on the input data.

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: (https://tinyurl.com/yabtw4y9)

maskedsku2018-08-01product_113380.82Input should have1. Have an unique identifier column called 'maskedsku'. eg. maskedsku can be your databaseID.2. The date format of the columns should be: ''YYYY-MM-DD HH:MM''

Output

• Content type: text/csv • Sample output file:(https://tinyurl.com/y8dgk2v6)

maskedsku2015-04-04201808022300_forecastproduct_113380.818800.8684030107

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 - 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

Cloud Storage Cost Forecasting

For any assistance, please reach out at:

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

AWS Infrastructure

AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services. Learn more

Refund Policy

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

Customer Reviews