Sold by: harpin AIÂ
Deployed on AWS
This product performs identity resolution on customer data from various sources to create accurate and complete customer profiles.
Overview
This product allows users to perform identity resolution on their own customer data from various data sources (e.g. bookings, transactions and loyalty program). The algorithm will link those different data sources to create an accurate and complete view of their customer profiles without moving any customer data outside of their aws account.
Highlights
- This product allows users to perform identity resolution on their customer data inside their own aws account.
- It provides field level mapping, normalization, standardization and repair out of the box. It utilizes the recent advancement of AI.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Training Recommended | Algorithm training on the ml.m5.2xlarge instance type | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Training | Algorithm training on the ml.m5.4xlarge instance type | $0.00 |
ml.m5.xlarge Training | Algorithm training on the ml.m5.xlarge instance type | $0.00 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
Update the underlying similarity model
Additional details
Inputs
- Summary
Either CSV or avro or parquet type is allowed for the clustering process. If CSV input files are used, each CSV file should be comma-delimited (,) and contain a header line at the top. Each row of a CSV file represents a single record, while each column represents a field. The following are the recommended fields: sourceRecordId, firstName, middleName, lastName, dateOfBirth, emailAddress, mobilePhone, homePhone, workPhone, postalCode, streetAddress, city, governingDistrict, ipAddress, accountId.
- Limitations for input type
- CSV, avro, or parquet
- Input MIME type
- csv, avro, parquet
record_id,given_name,sur_name,dob,email,phone,zip,street_address
101,John,Smith,19901010,john@gmail.com,5051234567,92128,123 main street
202,Joe,Matthew,20001010,joe@gmail.com,8581234567,92101,456 ace street
https://github.com/harpin-ai/toolkit-examples/sagemaker/sample_data_50k/data/
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
firstName | firstName: given name;
middleName: middle name;
lastName: surname | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
middleName | firstName: given name;
middleName: middle name;
lastName: surname | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
lastName | firstName: given name;
middleName: middle name;
lastName: surname | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
dateOfBirth | dateOfBirth: date of birth | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
emailAddress | emailAddress: email address | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
mobilePhone | mobilePhone: mobile phone;
homePhone: home phone;
workPhone: work phone | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
homePhone | mobilePhone: mobile phone;
homePhone: home phone;
workPhone: work phone | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
workPhone | mobilePhone: mobile phone;
homePhone: home phone;
workPhone: work phone | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
postalCode | postalCode: postal code;
streetAddress: street address;
city: city | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
streetAddress | postalCode: postal code;
streetAddress: street address;
city: city | Default value: BLANK
Type: FreeText
Limitations: None of the above fields are required, but there is a minimum information required in order to be able to uniquely identify each record. | No |
Resources
Vendor resources
Support
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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