Sold by: ElectrifAi LLCÂ
Deployed on AWS
Remove the need for manual data cleansing with our "Company Name" standardization algorithm
Overview
Leveraging 1st and 3rd party data, standardize and cleanse the common problem of company name variability. Regardless of acronyms, acquisitions, or other company evolutions, this algorithm will create a uniform naming convention for company names in your data.
Our machine learning models are available through a Private Offer. Please contact info@electrifai.net for subscription service pricing.
SKU: STAND-PT-PCM-AWS-001
Highlights
- Data inconsistency is a frequent big data problem, especially when you need an effective way to normalize company names. ElectrifAi's "Company Name" standardization algorithm solves this problem.
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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $700.00 |
ml.p2.xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.xlarge instance type, real-time mode | $500.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $900.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $500.00 |
ml.p2.16xlarge Inference (Real-Time) | Model inference on the ml.p2.16xlarge instance type, real-time mode | $700.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $900.00 |
Vendor refund policy
According to contract.
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Content disclaimer
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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
Vulnerability CVE-2021-3177 (i.e. https://nvd.nist.gov/vuln/detail/CVE-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input
Accepts csv files Supported content types: application/octet-stream
Invoking endpoint
AWS CLI Command aws sagemaker-runtime invoke-endpoint --endpoint-name $endpoint_name --body fileb://test_file.csv --content-type 'application/octet-stream' --region us-east-2 output.json
Replace the following parameters:
- endpoint_name – inference endpoint name
- test_file.csv - provide your input file name
- output.json – output file
- Input MIME type
- application/octet-stream
https://github.com/ElectrifAi/model-aws-company-name-standardization/blob/master/test_file.csv
https://github.com/ElectrifAi/model-aws-company-name-standardization/blob/master/test_file.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Supplier_Name | Accepts csv files
Supported content types: application/octet-stream | Type: FreeText | Yes |
Supplier_Name_Fix | Accepts csv files
Supported content types: application/octet-stream | Type: FreeText | Yes |
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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