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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Sentiment Analysis Free trial
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Latest Version:
2
SageMaker model package for Sentiment Analysis
Product Overview
This SageMaker model package provides a REST api to analyze the sentiment of English sentences. The API accepts input as JSON, CSV or plain text, and identifies the sentiment (positive or negative) and provides a confidence level (float number from 0 to 1). We welcome your feedback at aws-support@sigmodata.com
Key Data
Version
Show other versions
Type
Model Package
Highlights
State of the art sentiment analysis model.
Uses deep learning and neural networks to better interpret complex sentence syntaxes.
Very fast and responsive. Send hundreds of sentences at once and get sub-second response times.
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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$0.10/hr
running on ml.m5.xlarge
Model Batch Transform$0.10/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$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
About Free trial
Try this product for 1 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
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.m5.large | $0.10 | |
ml.m5.xlarge Vendor Recommended | $0.10 | |
ml.m5.2xlarge | $0.10 | |
ml.m5.4xlarge | $0.10 | |
ml.m5.12xlarge | $0.10 | |
ml.m5.24xlarge | $0.10 | |
ml.m4.xlarge | $0.10 | |
ml.m4.2xlarge | $0.10 | |
ml.m4.4xlarge | $0.10 | |
ml.m4.10xlarge | $0.10 | |
ml.m4.16xlarge | $0.10 | |
ml.c5.xlarge | $0.10 | |
ml.c5.2xlarge | $0.10 | |
ml.c5.4xlarge | $0.10 | |
ml.c5.9xlarge | $0.10 | |
ml.c5.18xlarge | $0.10 | |
ml.c4.xlarge | $0.10 | |
ml.c4.2xlarge | $0.10 | |
ml.c4.4xlarge | $0.10 | |
ml.c4.8xlarge | $0.10 | |
ml.p2.xlarge | $0.10 | |
ml.p2.8xlarge | $0.10 | |
ml.p2.16xlarge | $0.10 | |
ml.p3.2xlarge | $0.10 | |
ml.p3.8xlarge | $0.10 | |
ml.p3.16xlarge | $0.10 |
Usage Information
Model input and output details
Input
Summary
The model can extract sentiment out of input text. Provide a list of sentences to classify.
Limitations for input type
Maximum number of words per sentence is 200
Input MIME type
text/csv, application/jsonSample input data
[
"His behavior was unnaceptable",
"I feel pretty good about this"
]
Output
Summary
The sentiment is classified as positive or negative, and a confidence value is provided (0 to 1) with 1 being highly confident.
Output MIME type
application/jsonSample output data
{
"results": [
{
"confidence": 0.9142799377441406,
"input": "I feel great today",
"sentiment": "positive"
}
]
}
Sample notebook
Additional Resources
End User License Agreement
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Support Information
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 MoreRefund Policy
No refunds offered but you may cancel at any time
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