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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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Plug and Predict

By:
Latest Version:
v1.2.1
Platform for Automated Feature Engineering, Discovery and machine learning modeling at scale.

    Product Overview

    Plug and Predict enables you to get the best features and model for your prediction problem, automatically! Leveraging evolutionary algorithms and ensemble models, Plug and Predict sifts through the high-dimensional search space of features and models to figure out the best possible solution for your prediction problem. The only inputs needed are transactional data and the specifications for your prediction problem. Get the right features and model for any binary classification prediction problem involving transactional data.

    Key Data

    By
    Categories
    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • AI-powered feature engineering, discovery and modelling for prediction problems.

    • Easily integrated into your existing workflow via API call.

    • A sample dataset has been included in this to enable you to try Plug n Predict for free.

    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

    Annual$800,000.00/yr

    running on any instance

    Algorithm Training$350/hr

    running on ml.m5.12xlarge

    Model Realtime Inference$0.00/hr

    running on ml.m5.large

    Model Batch Transform$0.00/hr

    running on ml.m5.large

    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$2.765/host/hr

    running on ml.m5.12xlarge

    SageMaker Realtime Inference$0.115/host/hr

    running on ml.m5.large

    SageMaker Batch Transform$0.115/host/hr

    running on ml.m5.large

    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.m5.4xlarge
    $350.00
    ml.m5.12xlarge
    Vendor Recommended
    $350.00
    ml.m5.2xlarge
    $350.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Input and Output Details Please refer to Plug and Predict user guide for more details. Please reach out to ZS team engaged with client to get the latest user guide. Hyper tuning Parameters Please refer to Plug and Predict user guide for more details. Please reach out to ZS team engaged with client to get the latest user guide.

    Below are the recommended Instance types, number of instances and storage information for successful execution of algorithm.

    Auto Feature Transformer | Dataset | Generations | Instance type | # of Instances | storage/instance | |------------|---------------|--------------------|------------------|---------------------| | <= 10 GB | 50 | ml.m5.2xlarge | 10 | 40 GB | | <= 10 GB | 100 | ml.m5.4xlarge | 5 | 40 GB | | <= 10 GB | 200 | ml.m5.4xlarge | 10 | 40 GB | | 10-20 GB | 50 | ml.m5.12xlarge | 10 | 80 GB | | 10-20 GB | 100 | ml.m5.12xlarge | 10 | 80 GB | | 10-20 GB | 200 | ml.m5.12xlarge | 10 | 80 GB |

    Auto Feature Transformer | Dataset | Generations | Instance type | # of Instances | storage/instance | |------------|---------------|--------------------|------------------|---------------------| | <= 10 GB | 200 | ml.m5.4xlarge | 10 | 40 GB | | 10-20 GB | 200 | ml.m5.12xlarge | 10 | 80 GB |

    Rule Parser | Dataset | Instance Type | # of Instances | Storage/instance | |------------|------------------|-----------------|---------------------| | <= 10 GB | ml.m5.4xlarge | 5 | 40 GB |

    Channel specification

    Fields marked with * are required

    train

    Input modes: File
    Content types: -
    Compression types: -

    test

    Input modes: File
    Content types: -
    Compression types: -

    attribute_config

    Input modes: File
    Content types: -
    Compression types: -

    inference_model

    Input modes: File
    Content types: -
    Compression types: -

    event_code_mapping

    Input modes: File
    Content types: -
    Compression types: -

    Hyperparameters

    Fields marked with * are required

    generations

    Number of generations
    Type: Integer
    Tunable: No

    population_size

    Population size in each generation
    Type: Integer
    Tunable: No

    tournament_size

    Best candidates to move into next generation
    Type: Integer
    Tunable: No

    hgs_granularity

    Time search granularity
    Type: Integer
    Tunable: No

    reach_percentage_cutoff

    Minimum threshold for categorical variables
    Type: Continuous
    Tunable: No

    fitness_cutoff

    Fitness threshold
    Type: Continuous
    Tunable: No

    max_days

    Past days to consider
    Type: Integer
    Tunable: No

    stopping_criteria

    Early stopping criteria threshold value
    Type: Continuous
    Tunable: No

    candidate_singularity_flag

    Perform candidate singularity
    Type: Integer
    Tunable: No

    is_transform

    Set to true if only inference required
    Type: Categorical
    Tunable: No

    module

    Set to true if only inference required
    Type: Categorical
    Tunable: No

    is_debug

    Set to true if debug logs are required
    Type: Categorical
    Tunable: No

    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

    Plug and Predict

    Support will be provided by ZS team engaged with client

    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

    Refund policy is as per End User License Agreement.

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