Overview
Python predict
Detect and anonymize PII spans from a SageMaker endpoint
Python predict
POST /invocations
This SageMaker model package detects and anonymizes personally identifiable information (PII) in text. Built on Microsoft Presidio (Analyzer + Anonymizer) with spaCy en_core_web_lg, it identifies around 18 entity types by default - person names, email addresses, phone numbers, US Social Security numbers, credit card numbers, IBAN codes, IP addresses, URLs, crypto wallet addresses, and more - and lets you choose a different action for each: replace with a placeholder, mask with asterisks, redact entirely, hash (one-way, optionally with a caller-supplied salt for a stable join key), or encrypt (reversible, with a caller-supplied key that is never stored server-side).
Unlike a hosted PII API, this model runs entirely inside your own AWS account and VPC - the text you send it is never transmitted to an external service. That in-VPC data residency is the reason to run it here. GPU is not used; deploy on a CPU instance.
This listing is the SageMaker path (API only). For a self-hosted HTTP API, a point-and-click portal, CSV upload, an S3 batch queue, and POST /decrypt, use the PII Detector Server AMI.
Supports real-time inference and SageMaker batch transform, with JSON and CSV input/output. Common uses include redacting PII from support tickets, logs, and data exports before they reach a data warehouse or an LLM, and compliance workflows where sending customer text to a third-party PII API is not an option.
We welcome your feedback at aws-support@sigmodata.com .
Model and training data
Stock Presidio Analyzer + Anonymizer (MIT) with spaCy en_core_web_lg. Sigmodata did not fine-tune a detector for this version. The spaCy model is the public English large pipeline; regex and checksum recognizers (email, SSN, credit card, IBAN, IP, URL) are Presidio's defaults. The image runs in network isolation on SageMaker, so no data leaves your account.
Known limitations
- English only. Other languages are not supported.
- Organization, location, and date/time detection use general-purpose spaCy NER and are noisier than the checksum-backed types.
- Full postal-address detection is not a built-in capability.
- Phone recall on mixed international formats is weaker than on US-format numbers.
- Encrypt is supported, but this endpoint does not decrypt. Keep the caller-supplied key and the rewrites array and run Presidio's DeanonymizeEngine yourself, or use the PII Detector Server AMI (POST /decrypt).
Measured performance
- Evaluated on 1,505 held-out synthetic texts generated with presidio-evaluator (PresidioSentenceFaker). A prediction counts as correct when its character span overlaps the gold span of the same type (not an exact-span match).
- Strongest types (F1): EMAIL_ADDRESS 1.00, IBAN_CODE 1.00, US_SSN 0.97, IP_ADDRESS 0.94, URL 0.91, CREDIT_CARD 0.86, PERSON 0.81.
- Weaker types, not gated for this release: ORGANIZATION 0.52, LOCATION 0.48, DATE_TIME 0.37.
- Local CPU container of this image: about 180 texts per second at a batch size of 25 (6 ms median for a single short sentence).
- Live SageMaker realtime on ml.m5.large (single instance, sequential client, us-east-2): median 226 ms per short text (p90 234 ms); about 51 texts per second at a batch size of 25. Confirm on your endpoint if latency is tight.
Highlights
- Detect ~18 PII entity types - names, emails, SSNs, credit cards, IBANs, IPs, and more
- Choose replace, mask, redact, hash, or encrypt independently per entity type
- Runs entirely inside your VPC - text never leaves your AWS account
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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.large Inference (Real-Time) | Model inference on the ml.c5.large 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.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 |
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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.
Version release notes
Initial release.
- Detect ~18 default PII entity types (Microsoft Presidio + spaCy)
- Per-entity-type operator: replace, mask, redact, hash, or encrypt
- JSON and CSV input/output, real-time and batch transform
- CPU only - no GPU instance tier
Additional details
Inputs
- Summary
A JSON object with a "texts" array of strings to scan (or a CSV file, one text per line, for batch transform). Optional "entities" allowlist, "operators" map, and "language".
- Input MIME type
- application/json, text/csv
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 scan for PII | - | Yes |
entities | Optional allowlist of entity type names (for example ["EMAIL_ADDRESS", "US_SSN"]) | - | No |
operators | Optional per-entity-type action map (replace, mask, redact, hash, encrypt) | - | No |
language | Optional language code (default en) | - | No |
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Support contact: Email: aws-support@sigmodata.com Support URL: https://www.sigmodata.com
Support description: Support via email for deployment, configuration, and troubleshooting.
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