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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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Energy Consumption Forecasting

Latest Version:
3.3
The solution provides 30 months forecast of Energy Consumption using historical monthly Energy Consumption data.

    Product Overview

    Energy Consumption Forecasting generates 30 months of forward forecast of the consumption using historical data. 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

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • This solution will take in monthly data as input and provide 30 months 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 Forecasting can be applied for Energy Consumption.

    • Need customized Deep Learning and Machine Learning 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

    Model Realtime Inference$5.00/hr

    running on ml.m5.2xlarge

    Model Batch Transform$10.00/hr

    running on ml.m5.2xlarge

    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 Realtime Inference$0.461/host/hr

    running on ml.m5.2xlarge

    SageMaker Batch Transform$0.461/host/hr

    running on ml.m5.2xlarge

    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.m5d.24xlarge
    $5.00
    ml.c5d.large
    $5.00
    ml.m4.16xlarge
    $5.00
    ml.m5.2xlarge
    Vendor Recommended
    $5.00
    ml.r5d.large
    $5.00
    ml.c5d.4xlarge
    $5.00
    ml.m4.2xlarge
    $5.00
    ml.c5.2xlarge
    $5.00
    ml.c5d.9xlarge
    $5.00
    ml.c4.2xlarge
    $5.00
    ml.m4.10xlarge
    $5.00
    ml.c4.xlarge
    $5.00
    ml.m5.24xlarge
    $5.00
    ml.m5d.xlarge
    $5.00
    ml.m5d.large
    $5.00
    ml.c5.xlarge
    $5.00
    ml.m5.12xlarge
    $5.00
    ml.m5d.4xlarge
    $5.00
    ml.c4.4xlarge
    $5.00
    ml.c5.large
    $5.00
    ml.m5.xlarge
    $5.00
    ml.c5.9xlarge
    $5.00
    ml.m4.xlarge
    $5.00
    ml.c5.4xlarge
    $5.00
    ml.m5d.2xlarge
    $5.00
    ml.c5d.xlarge
    $5.00
    ml.m5d.12xlarge
    $5.00
    ml.c4.large
    $5.00
    ml.m5.large
    $5.00
    ml.c5d.18xlarge
    $5.00
    ml.r5.2xlarge
    $5.00
    ml.c5d.2xlarge
    $5.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Input

    • Supported content types: text/csv • Sample input file: (https://tinyurl.com/yctzvyt8 )

    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:(https://tinyurl.com/y7b5f5j2 )

    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:

    • "endpoint-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

    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

    Energy Consumption Forecasting

    For any assistance, please reach out at:

    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.

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