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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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Mphasis HyperGraf Big 5 Trait Analyzer

Latest Version:
4.1
Lexicon based NLP solution for predicting big 5 (OCEAN) personality traits and assigning trait scores to users.

    Product Overview

    The Big 5 personality analyser takes input text from a user and assigns score on the Big 5 personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism). The underlying algorithm utilises Natural Language Processing and computational psycho-linguistics to assign the scores.

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • The solution provides assessment of an individual's score on the Big 5 (OCEAN) personality traits. The algorithm analyses individual expressions in blogs, emails, chat transcripts, tweets, Facebook posts, comments made on social media platforms etc. and conducts a lexicon-based analysis to arrive at the 5 scores.

    • The solution provides a way to leverage information about personality traits to provide hyper personalized experiences to individuals interacting with firms in various capacities. For instance, as a customer seeking products, or a prospective employee applying for a job, or as existing employees exploring training for improving personal effectiveness.

    • Need customised Machine Learning and Deep Learning Solutions? Get in touch !Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP 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$4.00/hr

    running on ml.m5.xlarge

    Model Batch Transform$8.00/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 Realtime Inference$0.23/host/hr

    running on ml.m5.xlarge

    SageMaker Batch Transform$0.115/host/hr

    running on ml.m5.large

    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
    $4.00
    ml.g4dn.4xlarge
    $4.00
    ml.m5.4xlarge
    $4.00
    ml.m4.16xlarge
    $4.00
    ml.m5.2xlarge
    $4.00
    ml.p3.16xlarge
    $4.00
    ml.r5.large
    $4.00
    ml.g4dn.2xlarge
    $4.00
    ml.m4.2xlarge
    $4.00
    ml.r5.12xlarge
    $4.00
    ml.c5.2xlarge
    $4.00
    ml.r5.xlarge
    $4.00
    ml.p3.2xlarge
    $4.00
    ml.c4.2xlarge
    $4.00
    ml.g4dn.12xlarge
    $4.00
    ml.m4.10xlarge
    $4.00
    ml.c4.xlarge
    $4.00
    ml.m5.24xlarge
    $4.00
    ml.c5.xlarge
    $4.00
    ml.g4dn.xlarge
    $4.00
    ml.r5.24xlarge
    $4.00
    ml.p2.xlarge
    $4.00
    ml.m5.12xlarge
    $4.00
    ml.g4dn.16xlarge
    $4.00
    ml.p2.16xlarge
    $4.00
    ml.c4.4xlarge
    $4.00
    ml.r5.4xlarge
    $4.00
    ml.c5.large
    $4.00
    ml.m5.xlarge
    Vendor Recommended
    $4.00
    ml.c5.9xlarge
    $4.00
    ml.m4.xlarge
    $4.00
    ml.c5.4xlarge
    $4.00
    ml.p3.8xlarge
    $4.00
    ml.c4.large
    $4.00
    ml.m5.large
    $4.00
    ml.c4.8xlarge
    $4.00
    ml.p2.8xlarge
    $4.00
    ml.g4dn.8xlarge
    $4.00
    ml.t2.xlarge
    $4.00
    ml.c5.18xlarge
    $4.00
    ml.t2.large
    $4.00
    ml.r5.2xlarge
    $4.00
    ml.t2.medium
    $4.00
    ml.t2.2xlarge
    $4.00

    Usage Information

    Model input and output details

    Input

    Summary

    The algorithm works with any text data which could be in the form of a blogs, email, chat transcript, Facebook post, tweets, or any other text expression by the individual whose personality is being assessed.

    • The input must be in ‘.csv’ format.

    • The column containing the text data must be given the heading as “Text”

    • All the text data should contain atleast 200 words.,for better results one can provide more words.

    Input MIME type
    text/csv, text/plain
    Sample input data

    Output

    Summary
    • Output will give score to each personality trait between 1 to 5 depending on text data given as input.
    • Score of 1 denotes that the person is very low on that personality trait whereas a score of 5 denotes that the person is very high on that personality trait.
    • Output is in the form of a ‘.csv’ file.
    Output MIME type
    text/csv, text/plain
    Sample output data

    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

    Mphasis HyperGraf Big 5 Trait Analyzer

    For any assistance, please reach out at:

    AWS Infrastructure

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    Refund Policy

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