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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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Identity Resolution with AI and ML

By:
harpin AI
Latest Version:
1.1.6
This product performs identity resolution on customer data from various sources to create accurate and complete customer profiles.
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    Product Overview

    This product allows users to perform identity resolution on their own customer data from various data sources (e.g. bookings, transactions and loyalty program). The algorithm will link those different data sources to create an accurate and complete view of their customer profiles without moving any customer data outside of their aws account.

    Key Data

    Version
    1.1.6
    Usage Instructions
    Release Notes
    By
    harpin AI
    Categories
    ML Solutions
    Clustering-Structured
    Master Data Management
    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • This product allows users to perform identity resolution on their customer data inside their own aws account.

    • It provides field level mapping, normalization, standardization and repair out of the box. It utilizes the recent advancement of AI.

    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.m5.2xlarge

    Model Batch Transform$0.00/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 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

    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
    $0.00
    ml.m5.2xlarge
    Vendor Recommended
    $0.00
    ml.m5.xlarge
    $0.00

    Usage Information

    Training

    The training actually performs a clustering process in this particular case. It clusters the records in the input datasets into a set of dis-joint customer profiles. Each of the input datasets should be provided as a folder (i.e. S3 folder) containing one or more CSV or avro or parquet files. Note that only one file type (i.e. file extension) is allowed in the folder or a single data source.

    Channel specification

    Fields marked with * are required

    clustering

    *
    The clustering input file
    Input modes: File
    Content types: csv, avro, parquet
    Compression types: None

    clustering2

    The 2nd clustering input file
    Input modes: File
    Content types: csv, avro, parquet
    Compression types: None

    clustering3

    The 3rd clustering input file
    Input modes: File
    Content types: csv, avro, parquet
    Compression types: None

    channel_config

    *
    The data source configuration file for all channels
    Input modes: File
    Content types: yaml
    Compression types: None

    Model input and output details

    Input

    Summary

    Either CSV or avro or parquet type is allowed for the clustering process. If CSV input files are used, each CSV file should be comma-delimited (,) and contain a header line at the top. Each row of a CSV file represents a single record, while each column represents a field. The following are the recommended fields: sourceRecordId, firstName, middleName, lastName, dateOfBirth, emailAddress, mobilePhone, homePhone, workPhone, postalCode, streetAddress, city, governingDistrict, ipAddress, accountId.

    Limitations for input type
    CSV, avro, or parquet
    Input MIME type
    text/csv
    Sample input data
    record_id,given_name,sur_name,dob,email,phone,zip,street_address
101,John,Smith,19901010,john@gmail.com,5051234567,92128,123 main street
202,Joe,Matthew,20001010,joe@gmail.com,8581234567,92101,456 ace street
    Expand all input descriptions
    Field names
    firstName, middleName, lastName
    Description
    firstName: given name; middleName: middle name; lastName: surname
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field name
    dateOfBirth
    Description
    dateOfBirth: date of birth
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field name
    emailAddress
    Description
    emailAddress: email address
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field names
    mobilePhone, homePhone, workPhone
    Description
    mobilePhone: mobile phone; homePhone: home phone; workPhone: work phone
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field names
    postalCode, streetAddress, city
    Description
    postalCode: postal code; streetAddress: street address; city: city
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field name
    ipAddress
    Description
    ipAddress: ip address
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field name
    accountId
    Description
    accountId: customer account identifier
    Required
    No
    Data type
    FreeText
    Default value
    BLANK
    Limitations
    None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record.
    Field name
    sourceRecordId
    Description
    This is a required field, which uniquely identifies each of the records in the data source.
    Required
    Yes
    Data type
    FreeText
    Limitations
    Maximum 64 characters

    Output

    Summary

    The training (i.e. clustering in this case) process produces an identity graph which will be stored in a folder with one or more files with the exact same type as the input files. If the input files are CSVs, then the output will contains CSV files too. All the fields in the input files will be retained in the output files, along with one additional field called PIN. The field PIN is the assigned unique customer profile identitfier.

    Output MIME type
    text/csv
    Sample output data
    Expand all output descriptions
    Field names
    sourceRecordId, firstName, middleName, lastName, dateOfBirth, emailAddress, mobilePhone, homePhone, workPhone, postalCode, streetAddress, city, ipAddress, accountId
    Description
    The output data - identity graph - will contain the union of fields from all the input data sources, plus an additional field "pin".
    Always returned
    Yes
    Data type
    FreeText
    Field name
    PIN
    Description
    The output data includes all the input fields plus one additional field PIN. PIN is the assigned unique customer profile identitfier. The default value for PIN is -1. Customer records with the same (non-default) PIN are considered to be referring to the same customer profile.
    Always returned
    No
    Data type
    Integer
    Range
    Default value
    The default value for PIN is -1, meaning that there is not enough information available in the input record to determine which customer profile it belongs to.

    Sample notebook

    Additional Resources

    End User License Agreement

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    Support Information

    AWS Infrastructure

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    Refund Policy

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

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