Sold by: MphasisÂ
Deployed on AWS
This solution identifies data belonging to the same entity/duplicate records across two data sources and creates a linked master dataset.
Overview
This solution intelligently links & combines records from two related data sources into a single master record. The module acts as a crucial component in Master Data Management. It assigns a common ID to records belonging to the same entity/duplicates. The probabilistic linkage algorithm provides the flexibility to find duplicate records based on a required match percentage given the columns of interest. The Data Linkage module is applicable in scenarios such as: (a) Linking medical records of patients from different health care providers (b) Linking information from different external data sources (c) Linking census records of persons over a period of time. (d) Linking consumer product information from different sources (reviews, sellers etc.,)
Highlights
- Data collected on any entity from different sources can have different formatting conventions, errors, missing values and even recorded at different time periods. Linking such sources poses a challenge. The probabilistic data linkage algorithm solves this problem by computing a match probability between entities from two sources. Based on a required threshold, one can identify/link the same entity from the two sources of data.
- The algorithm is un-supervised thereby reducing the time and cost of acquiring labelled data for training/re-training the algorithm. The module integrates well with ETL processes and can act as a critical component in MDM.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $20.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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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
- Improvement in matching through enhanced data preprocessing
Additional details
Inputs
- Summary
The solution takes a zip file as input.
The zip must conatin:
two 'CSV' files corresponding to the two sources of data and "config.json: - a configuration file. (List of column pairs for linking/deduplication along with %threshold match required) Ensure Content-Type is 'application/zip'
input.zip config.json dataset1.csv dataset2.csv
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Data-Deduplication-Record-Linkage/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Data-Deduplication-Record-Linkage/tree/main/Input
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to:
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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