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.
Quantum Simulator: Content Clustering
By:
Latest Version:
1.2
Quantum simulator based content clustering solution designed to cluster coherent news headlines.
Product Overview
Quantum simulator based content clustering solution which clusters coherent news articles in one cluster. The simulator runs on quantum annealing algorithm (SQA) to solve optimization problem. Clustering is an unsupervised ML problem in which all the coherent data points are part of same cluster and each data point can be part of only one cluster. We are formulating clustering as a constraint satisfaction optimization problem and solving it using Quantum Annealers.
Key Data
Version
By
Type
Model Package
Highlights
Documents contain clusters of topics which represent distribution of coherent words. This solution clusters given set of documents based on most relevant topics using NLP and clustering. Quantum annealers reduce the time and space required to solve cluster problems and provides better quality results.
Application of clustering include document indexing, understanding distribution of data, abstract of the document, document similarity, enterprise content search, Search Engine Optimization (SEO) and Real Time Analysis (RTA) on journals, reports, news, social media posts, customer reviews, emails and surveys.
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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$20.00/hr
running on ml.t2.large
Model Batch Transform$40.00/hr
running on ml.m5.xlarge
Infrastructure PricingWith 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
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$0.111/host/hr
running on ml.t2.large
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
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 | $20.00 | |
ml.m5.4xlarge | $20.00 | |
ml.m5d.24xlarge | $20.00 | |
ml.c5d.large | $20.00 | |
ml.m4.16xlarge | $20.00 | |
ml.m5.2xlarge | $20.00 | |
ml.p3.16xlarge | $20.00 | |
ml.c5d.4xlarge | $20.00 | |
ml.m4.2xlarge | $20.00 | |
ml.c5.2xlarge | $20.00 | |
ml.c5d.9xlarge | $20.00 | |
ml.p3.2xlarge | $20.00 | |
ml.c4.2xlarge | $20.00 | |
ml.m4.10xlarge | $20.00 | |
ml.c4.xlarge | $20.00 | |
ml.m5.24xlarge | $20.00 | |
ml.m5d.xlarge | $20.00 | |
ml.m5d.large | $20.00 | |
ml.c5.xlarge | $20.00 | |
ml.p2.xlarge | $20.00 | |
ml.m5.12xlarge | $20.00 | |
ml.m5d.4xlarge | $20.00 | |
ml.p2.16xlarge | $20.00 | |
ml.c4.4xlarge | $20.00 | |
ml.c5.large | $20.00 | |
ml.m5.xlarge | $20.00 | |
ml.c5.9xlarge | $20.00 | |
ml.m4.xlarge | $20.00 | |
ml.c5.4xlarge | $20.00 | |
ml.m5d.2xlarge | $20.00 | |
ml.c5d.xlarge | $20.00 | |
ml.p3.8xlarge | $20.00 | |
ml.m5d.12xlarge | $20.00 | |
ml.c4.large | $20.00 | |
ml.m5.large | $20.00 | |
ml.c4.8xlarge | $20.00 | |
ml.p2.8xlarge | $20.00 | |
ml.t2.xlarge | $20.00 | |
ml.c5.18xlarge | $20.00 | |
ml.c5d.18xlarge | $20.00 | |
ml.t2.large Vendor Recommended | $20.00 | |
ml.t2.medium | $20.00 | |
ml.t2.2xlarge | $20.00 | |
ml.c5d.2xlarge | $20.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input:
- Supported content type:
text/csv - Inputfile should be a csv file with not more then 500 datapoints.
- File size should not exceed 300 KB
- Csv file should contain a column name- "sentence" which will have all sentences which are to be clustered.
Output:
Instructions for score interpretation:
- Content type:
application/json - Final result is in json format which will contain 3 keys 'r', 'g' , b' which denotes 3 clusters and each key will have all the sentences corresponding to that cluster.
- Currently our quantum simulater detects 3 clusters.
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 $model_name --body fileb://$file_name --content-type 'text/csv' --region us-east-2 output.jsonSubstitute the following parameters:
"model-name"- name of the inference endpoint where the model is deployedfile_name- input zip file nametext/csv- type of the given inputoutput.json- filename where the inference results are written to
Resources:
Additional Resources
End User License Agreement
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Support Information
Quantum Simulator: Content Clustering
For any assistance reach out to us at:
AWS Infrastructure
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Learn MoreRefund Policy
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