Sold by: Sigmodata
Deployed on AWS
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SageMaker model package for classifying news text into topics
Overview
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Python predict
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POST /invocations
This SageMaker model package provides a REST API to classify news text into four topics: World, Sports, Business, or Sci/Tech, with a confidence score.
Use it to organize content feeds, tag articles at ingestion time, or build topic-aware recommendation and monitoring pipelines.
The API accepts input as JSON or CSV and supports real-time endpoints and batch transform.
We welcome your feedback at aws-support@sigmodata.com
Highlights
- Tag incoming news as World, Sports, Business, or Sci/Tech as it hits the ingest pipe
- A four-way news taxonomy with confidence - not a generic topic model you have to interpret
- JSON or CSV; real-time or batch; no training job
Details
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Pricing
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Try this product free for 5 days according to the free trial terms set by the vendor.
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 | $0.10 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.10 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.10 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.10 |
ml.c5.xlarge Inference (Batch) | Model inference on the ml.c5.xlarge instance type, batch mode | $0.10 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
Dimensions summary
You pay by the hour based on the SageMaker instance you run. The five instance types (ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.c5.xlarge, ml.c5.2xlarge) span general-purpose and compute-optimized families, letting you match capacity to your workload. Each instance comes in two modes: batch, for processing groups of text at once, and real-time, for live endpoint requests. This gives you 10 combinations. Pricing scales with instance size and mode, so cost tracks the hours each endpoint or batch job runs. The underlying AWS infrastructure is billed separately.
Top-of-mind questions for buyers
One host hour is one hour that a chosen SageMaker instance runs your model. Real-time endpoints accrue hours while the endpoint stays live and waiting for requests. Batch jobs accrue hours only while the job runs. Each running instance is metered separately, so multiple instances multiply the hours.
A real-time endpoint accrues host hours whenever it stays deployed, even with no incoming text. To stop software charges, delete the endpoint. A batch job charges only for the hours it actively runs, then stops. Underlying AWS infrastructure is billed separately by AWS.
Both modes meter the same instance by the hour, but the trigger differs. Real-time bills for continuous endpoint uptime, so it suits steady live traffic. Batch bills only during a processing job, so it suits scoring large groups of text on demand. You pick per instance type.
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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
- First release
- JSON and CSV input and output
- ModernBERT fine-tuned model served with ONNX Runtime
Additional details
Inputs
- Summary
A JSON object with a "texts" array of strings to classify (or a CSV file, one text per line, for batch transform).
- Input MIME type
- text/csv, application/json
{"texts": ["The UN security council voted on the resolution"]}
The UN security council voted on the resolution
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
texts | List of text strings to classify | - | Yes |
Resources
Vendor resources
Support
Vendor support
contact: support@sigmodata.com
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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