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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 DeepInsights Keyphrase Extractor

Latest Version:
3.4
Machine Learning based solution which extracts important Key phrases/words from a corpus of text.

    Product Overview

    Key phrase extractor uses end-to-end text extraction pipeline, text analysis and natural language processing techniques to automate key phrases/words extraction from text documents. This solution is based on unsupervised graph-based, topic-based, statistics-based algorithms for the construction of word network and ranking in order to identify the most relevant keyphrases.

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • This solution provides a list of most relevant keyphrases present in a text document using a graph-based, topic based and statistics based ranking model.

    • Applications of key word extraction includes understanding of data, indexing, search, and scalability of content. Few use cases of keyword extraction are Search Engine Optimization (SEO) and Real Time Analysis (RTA) on social media posts, customer reviews, emails, chat transcripts and surveys.

    • Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities.Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning 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.large

    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$0.115/host/hr

    running on ml.m5.large

    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.m5d.24xlarge
    $4.00
    ml.m5.2xlarge
    $4.00
    ml.c5d.4xlarge
    $4.00
    ml.c4.2xlarge
    $4.00
    ml.m4.10xlarge
    $4.00
    ml.m5d.large
    $4.00
    ml.m5d.4xlarge
    $4.00
    ml.c4.4xlarge
    $4.00
    ml.m5.xlarge
    $4.00
    ml.c5.9xlarge
    $4.00
    ml.m5d.12xlarge
    $4.00
    ml.c4.large
    $4.00
    ml.r5.2xlarge
    $4.00
    ml.m5.4xlarge
    $4.00
    ml.c5d.large
    $4.00
    ml.m4.16xlarge
    $4.00
    ml.r5d.large
    $4.00
    ml.m4.2xlarge
    $4.00
    ml.c5.2xlarge
    $4.00
    ml.c5d.9xlarge
    $4.00
    ml.c4.xlarge
    $4.00
    ml.m5.24xlarge
    $4.00
    ml.m5d.xlarge
    $4.00
    ml.c5.xlarge
    $4.00
    ml.m5.12xlarge
    $4.00
    ml.c5.large
    $4.00
    ml.m4.xlarge
    $4.00
    ml.c5.4xlarge
    $4.00
    ml.m5d.2xlarge
    $4.00
    ml.c5d.xlarge
    $4.00
    ml.m5.large
    Vendor Recommended
    $4.00
    ml.c5d.18xlarge
    $4.00
    ml.c5d.2xlarge
    $4.00

    Usage Information

    Fulfillment Methods

    Amazon SageMaker

    Usage Methodology for the algorithm:

    • The input has to be a '.txt' file with 'utf-8' encoding. PLEASE NOTE: If your input .txt file is not 'utf-8' encoded, model will not perform as expected
    • To make sure that your input file is 'UTF-8' encoded please 'Save As' using Encoding as 'UTF-8'
    • Input should have atleast 3 sentences with 50 words (Model limitation)
    • The input can have a maximum of 750 words (Sagemaker restriction)
    • Supported content types: text/plain

    Input

    Supported content types: text/plain

    sample input:

    Uttar Pradesh Chief Minister Yogi Adityanath on Friday flagged off the Tejas Express, the country's first "private" train run by its subsidiary IRCTC, on the Lucknow-New Delhi route. The commercial run of the train starts on Saturday. The Tejas Express cuts the time travelled between the two cities to 6.15 hours from the 6.40 hours taken by the Swarn Shatabdi, currently the fastest train on the route."It is the first corporate train of the country.......

    Output

    Content type: text/csv

    sample output:

    SNo-|------Key Topics--------------------------------

    1. environment friendly public transport
    2. fastest train
    3. first corporate train
    4. minister piyush
    5. tejas express

    Invoking endpoint

    AWS CLI Command

    You can invoke endpoint using AWS CLI:

    aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'text/plain' --region us-east-2 output.csv

    Substitute the following parameters:

    • $model_name - name of the inference endpoint where the model is deployed
    • $file_name - 'input.txt'- input file to do the inference on
    • text/plain - type of the given input file (above)
    • output.csv - filename where the inference results are written to

    Resources

    Sample Jupyter Notebook Sample Input Sample Output

    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 DeepInsights Keyphrase Extractor

    For any assistance, please reach out to:

    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

    Currently we do not support refunds, but you can cancel your subscription to the service at any time.

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