Sold by: ElectrifAi LLCÂ
Overview
Reduce the manual effort to clean and group company names by identifying which companies are subsidiaries of other companies. Technical highlights include applying Convolutional Neural Networks (CNN), fuzzy matching techniques, and key collision. Using the data input of procurement transaction records, the model outputs a suggested group name for each vendor name. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: VNDNG-PS-PCM-AWS-001
Highlights
- Reduce the manual effort to clean and group company names by identifying which companies are subsidiaries of other companies.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
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
This product is available free of charge. Free 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.
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 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: One comma separated (csv) file. Reference file: sample.csv
- Input MIME type
- text/csv
https://github.com/ElectrifAi/model-aws-vendor-name-grouping/blob/master/sample.csv
https://github.com/ElectrifAi/model-aws-vendor-name-grouping/blob/master/sample.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
One comma separated (csv) file. Reference file: sample.csv | Input file contains two fields: Vendor_Name_raw and Spend_Amount | 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.
Similar products
This image is the fastest way to deploy VICIdial® the Open Source Contact center solution on AWS EC2 in One-Click
Threat researchers lose confidence in making smart business decisions when sources lack the quality, variety, and depth of threat intelligence needed to prevent disruption. SecurityScorecard's Attack Surface Intelligence (ASI) detects more unknown unknowns, including those of your third-party vendors and how they pose a risk to your business, arming you with deep contextual insights and attribution to prioritize your next steps - all in one single platform.
This is a Jenkins Build Agent node optimized for .Net build jobs. Use this image to quickly deploy a build agent for your Jenkins Server. Doing CI/CD has never been this easy. Launch your agent today.
By applying Cumulative Sum (CUSUM) theory to detect whether the quality of a process is normal, improving or deteriorating, this service calculates a Precision Weighted Excess Return (PWER) score, along with other statistics, to measure if a particular investment fund or investment management firm are more likely to outperform peer group investment funds on a consistent basis, and by how much.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.