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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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Time Series Anomaly Detection (LSTM-AE) Free trial

Latest Version:
1.5
Perform time series anomaly detection in SageMaker with Long Short-Term Memory autoencoders.

    Product Overview

    This algorithm performs time series anomaly detection with a Long Short-Term Memory Network Autoencoder (LSTM-AE). It implements both training and inference from CSV data and supports both CPU and GPU instances. The training and inference Docker images were built by extending the PyTorch 2.1.0 Python 3.10 SageMaker containers. The algorithm can be used for both univariate and multivariate time series.

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • The algorithm performs time series anomaly detection with the LSTM-AE model directly from CSV data.

    • The algorithm allows tuning the model hyperparameters to optimize performance on custom datasets.

    • The algorithm supports CPU, GPU and multi-GPU training.

    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

    Algorithm Training$4.99/hr

    running on ml.m5.2xlarge

    Model Realtime Inference$0.99/hr

    running on ml.m5.2xlarge

    Model Batch Transform$4.99/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 Algorithm Training$0.461/host/hr

    running on ml.m5.2xlarge

    SageMaker Realtime Inference$0.461/host/hr

    running on ml.m5.2xlarge

    SageMaker Batch Transform$0.461/host/hr

    running on ml.m5.2xlarge

    About Free trial

    Try this product for 5 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    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
    $4.99
    ml.c5n.18xlarge
    $4.99
    ml.g4dn.4xlarge
    $4.99
    ml.m5.4xlarge
    $4.99
    ml.m4.16xlarge
    $4.99
    ml.m5.2xlarge
    Vendor Recommended
    $4.99
    ml.p3.16xlarge
    $4.99
    ml.g5.xlarge
    $4.99
    ml.g5.12xlarge
    $4.99
    ml.g4dn.2xlarge
    $4.99
    ml.g5.4xlarge
    $4.99
    ml.c5n.xlarge
    $4.99
    ml.m4.2xlarge
    $4.99
    ml.c5.2xlarge
    $4.99
    ml.p3.2xlarge
    $4.99
    ml.c4.2xlarge
    $4.99
    ml.g4dn.12xlarge
    $4.99
    ml.p4d.24xlarge
    $4.99
    ml.m4.10xlarge
    $4.99
    ml.c4.xlarge
    $4.99
    ml.m5.24xlarge
    $4.99
    ml.c5.xlarge
    $4.99
    ml.g4dn.xlarge
    $4.99
    ml.g5.48xlarge
    $4.99
    ml.p2.xlarge
    $4.99
    ml.m5.12xlarge
    $4.99
    ml.g4dn.16xlarge
    $4.99
    ml.p2.16xlarge
    $4.99
    ml.c4.4xlarge
    $4.99
    ml.g5.8xlarge
    $4.99
    ml.m5.xlarge
    $4.99
    ml.c5.9xlarge
    $4.99
    ml.g5.16xlarge
    $4.99
    ml.m4.xlarge
    $4.99
    ml.c5.4xlarge
    $4.99
    ml.p3.8xlarge
    $4.99
    ml.m5.large
    $4.99
    ml.c4.8xlarge
    $4.99
    ml.c5n.2xlarge
    $4.99
    ml.p2.8xlarge
    $4.99
    ml.g4dn.8xlarge
    $4.99
    ml.c5n.9xlarge
    $4.99
    ml.c5.18xlarge
    $4.99
    ml.g5.2xlarge
    $4.99
    ml.c5n.4xlarge
    $4.99
    ml.g5.24xlarge
    $4.99

    Usage Information

    Training

    The input dataset should be provided as a CSV file. The training and validation datasets should only contain normal data (i.e. without anomalies). Each column of the CSV file represents a time series, while each row represents a time step. All the time series should have the same length and should not contain missing values. The CSV file should not contain any index column or column headers.

    Metrics

    Name
    Regex
    train_mse
    train:mse ([0-9\\.]+)
    train_mae
    train:mae ([0-9\\.]+)
    valid_mse
    valid:mse ([0-9\\.]+)
    valid_mae
    valid:mae ([0-9\\.]+)

    Channel specification

    Fields marked with * are required

    training

    *
    Input modes: File
    Content types: text/csv
    Compression types: None

    validation

    Input modes: File
    Content types: text/csv
    Compression types: None

    Hyperparameters

    Fields marked with * are required

    sequence-length

    *
    The length of the sequences
    Type: Integer
    Tunable: Yes

    sequence-stride

    *
    The period between consecutive sequences
    Type: Integer
    Tunable: Yes

    hidden-size

    *
    The number of hidden units of the LSTM layers
    Type: Integer
    Tunable: Yes

    lr

    *
    The learning rate used for training
    Type: Continuous
    Tunable: Yes

    batch-size

    *
    The batch size used for training
    Type: Integer
    Tunable: Yes

    epochs

    *
    The number of training epochs
    Type: Integer
    Tunable: Yes

    Model input and output details

    Input

    Summary

    The inference algorithm takes as input a CSV file containing the time series. Each column of the CSV file represents a time series, while each row represents a time step. The CSV file should not contain any index column or column headers. All the time series should have the same length and should not contain missing values.

    Input MIME type
    text/csv
    Sample input data

    Output

    Summary

    The inference algorithm outputs the anomaly scores and the reconstructed values of the time series. The anomaly scores are included in the first column, while the reconstructed values of the time series are included in the subsequent columns.

    Output MIME type
    text/csv
    Sample output data

    Additional Resources

    End User License Agreement

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    Support Information

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