Sold by: Mphasis
Deployed on AWS
An ML based solution to group a corpus of documents into clusters based on topics
Overview
Document grouping is a solution based on unsupervised machine learning that takes textual information and identifies topics across the given text corpus. Documents are grouped based on similarity of syntactic and contextual information present in them. This model takes a maximum of 30 documents (with each under 10Kb) as input and groups them into optimal number of clusters.
Highlights
- Document de-duplication, archiving and automatic organization of knowledge repositories are some of the use cases for this algorithm.
- A generic unsupervised machine learning framework to group documents based on information similarity that does not require prior curation of data.
- 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!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
Dimensions summary
You pay by the hour for each machine you run, based on the AWS instance type you choose. Pricing splits into two modes. Batch inference processes documents in scheduled groups, while real-time inference handles requests as they arrive. Real-time mode offers more instance choices, including compute-, memory-, GPU-, and burstable-optimized families. Within each mode, hourly rates rise with instance size and power, so smaller instances cost less per hour and larger or GPU-backed instances cost more. You select the mode and instance that fit your document grouping workload and control cost by how long each runs.
Top-of-mind questions for buyers
One HostHrs unit is one hour of running the software on a single instance of the type you choose. Billing meters actual running time. Partial hours and multiple concurrent instances each add to the count. You are charged per instance for each hour it stays active.
Batch mode processes documents in scheduled groups, so you run an instance only during a job. Real-time mode keeps an instance running to handle requests as they arrive. Both meter by the hour. Batch suits periodic workloads; real-time suits continuous request handling where the instance stays live.
Software charges accrue only while an instance runs. A stopped instance stops the hourly meter. This helps batch users, who can shut down between scheduled jobs. Note that underlying AWS storage or other resources may still bill separately, but the software meter counts running hours only.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
-
The input file should be a zip of text files (.txt) in utf-8 encoding
-
The zipped file can have a maximum of 30 documents
-
The maximum size of the each file should be <= 10KB (1000 lines)
-
Supported Content type: application/zip
Output
-
The output from the model is a json file, supported content type: application/json
-
The processed output is a json file which has lists of documents clusters, each representing the documents grouped on the basis of similar information
-
Sample output file:
{
“Cluster –1” : [“Doc-1”, “Doc-6”, “Doc-3”, “Doc-7”, “Doc-8”],
“Cluster –2” : [“Doc-2”, “Doc-4”]
}
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.zip --content-type application/zip --accept application/json result.jsonSubstitute the following parameters:
-
endpoint-name - name of the inference endpoint where the model is deployed
-
input.zip - input file
-
application/zip - MIME type of the given input file (above)
-
result.json - filename where the inference results are written to.
Resources
-
- Input MIME type
- application/zip
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
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.
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