Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”
Sign in
Sign in
or
Create a new account
English
Français
About
Categories
Delivery Methods
Solutions
Resources
 Your Saved List Become a Channel Partner 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.

product logo

Plug and Predict

By:
ZS
Latest Version:
v1.3
Platform for Automated Feature Engineering, Discovery and machine learning modeling at scale.
Continue to Subscribe

    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

    Version
    v1.3
    Usage Instructions
    Release Notes
    Show other versions
    By
    ZS
    Categories
    ML Solutions
    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.


    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$0.00/hr

    running on ml.m5.2xlarge

    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$0.461/host/hr

    running on ml.m5.2xlarge

    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.c5n.18xlarge
    $0.00
    ml.m4.4xlarge
    $0.00
    ml.m5.4xlarge
    $0.00
    ml.m4.16xlarge
    $0.00
    ml.m5.2xlarge
    Vendor Recommended
    $0.00
    ml.c5n.xlarge
    $0.00
    ml.m4.2xlarge
    $0.00
    ml.c5.2xlarge
    $0.00
    ml.c4.2xlarge
    $0.00
    ml.c4.xlarge
    $0.00
    ml.m4.10xlarge
    $0.00
    ml.m5.24xlarge
    $0.00
    ml.c5.xlarge
    $0.00
    ml.m5.12xlarge
    $0.00
    ml.c4.4xlarge
    $0.00
    ml.c5.9xlarge
    $0.00
    ml.m5.xlarge
    $0.00
    ml.c5.4xlarge
    $0.00
    ml.m4.xlarge
    $0.00
    ml.c4.8xlarge
    $0.00
    ml.m5.large
    $0.00
    ml.c5n.2xlarge
    $0.00
    ml.c5n.9xlarge
    $0.00
    ml.c5.18xlarge
    $0.00
    ml.c5n.4xlarge
    $0.00

    Usage Information

    Training

    Input and Output Details *Feature Generator *

    train – Train Dataset test – Test Dataset attribute_config – json file with definitions of the attributes/columns of the input data Modelling

    train – Train Dataset test – Test Dataset attribute_config – json file with modelling hyperparameters adaptive_pu_model – trained adaptive pu model Prediction

    new_data – the data to run prediction on trained_model_archive – pre-trained model attribute_config – json file with additional predication parameters

    Channel specification

    Fields marked with * are required

    train

    string
    Input modes: File
    Content types: application/x-parquet
    Compression types: None

    test

    string
    Input modes: File
    Content types: application/x-parquet
    Compression types: None

    attribute_config

    string
    Input modes: File
    Content types: json
    Compression types: None

    adaptive_pu_model

    string
    Input modes: File
    Content types: application/x-tar
    Compression types: None

    trained_model_archive

    string
    Input modes: File
    Content types: application/x-tar
    Compression types: None

    new_data

    string
    Input modes: File
    Content types: application/x-parquet
    Compression types: None

    Hyperparameters

    Fields marked with * are required

    module

    *
    Module Name to trigger
    Type: Categorical
    Tunable: No

    generations

    Number of iterations to execute
    Type: Integer
    Tunable: No

    population_size

    Number of features to be evaluated in each generation
    Type: Integer
    Tunable: No

    tournament_size

    Number of features to be evolved in the next generation
    Type: Integer
    Tunable: No

    max_days

    Number of days of history present in the data
    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

    combination_features_flag

    Create features based on the occurrence of different events in combination
    Type: Categorical
    Tunable: No

    no_of_top_events

    Number of top events to be considered for combination features
    Type: Integer
    Tunable: No

    no_of_classes

    Number of classifications
    Type: Integer
    Tunable: No

    get_domain_features

    Create domain features
    Type: Categorical
    Tunable: No

    get_lf_features

    Create look forward features
    Type: Categorical
    Tunable: No

    is_debug

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

    is_transform

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

    Model input and output details

    Input

    Summary

    NA

    Input MIME type
    application/x-parquet
    Sample input data
    NA
    Expand all input descriptions
    Field name
    NA
    Description
    NA
    Constraints

    Default value: NA Type: FreeText

    Required
    No

    Output

    Summary

    For each of the module, a model.tar.gz file would be generated inside the output folder on user defined s3 path. User can see the train and test result for feature generator and modeling modules and the prediction on the new data for prediction module. For modeling, the user can also find the trained model in pickle format.

    Sample output data
    NA

    Sample notebook

    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 https://www.zs.com/

    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

    This product is offered for free. If there are any questions, please contact us for further clarifications.

    Customer Reviews

    There are currently no reviews for this product.
    View all