Sold by: MphasisÂ
Deployed on AWS
Given a company name and the no. of companies to rank, this ML solution provides top companies with similar descriptions as output.
Overview
Search applications are ubiquitous in today's digital world across various use cases of plagiarism systems, ranking products, items, text descriptions etc. This is a text search solution that is based on efficient index based approximate nearest neighbours algorithm which is much faster than a brute force search algorithm. It can be integrated with existing systems or applications which have large text data bases and a need to look for similar text descriptions. One of the applications of this solution is to search for potentially similar companies based on their description when conducting competitive market analysis.
Highlights
- This solution is tested on publicly available company descriptions dataset. It uses efficient indexing based on state of the art text similarity search algorithms and can be utilized for creating search indexes for any large text databases like news articles, websites, user profiles, blogs, product descriptions etc.
- This solution is primarily focused on textual descriptive data but can be repurposed for other use cases like image search, neural (multi-modal) search etc. It can aid in building plagiarism check tools, recommendation systems, ranking systems, search engines, etc.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
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
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 |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $10.00/host/hour |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00/host/hour |
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
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
v1.0.0
Additional details
Inputs
- Summary
The trained model expects the following as input query in a json file called "model_input.json". It should contain the necessary keys as mentioned in input data descriptions.
- Limitations for input type
- The RAM requirement for performing the company description similarity search on a 2000 companies dataset is less than 8GiB (m5.large). Please setup your AWS instance according to your requirement.
- Input MIME type
- application/json
https://github.com/Mphasis-ML-Marketplace/Search-Similar-Company-Descriptions/tree/main/Model%20Input
https://github.com/Mphasis-ML-Marketplace/Search-Similar-Company-Descriptions/tree/main/Model%20Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
company_name | The name of the company for which we need similar companies. | Type: FreeText
Limitations: Provide only one company name for each inferencing job. | Yes |
k | The number of similar companies expected in output (k). | Type: Integer
Minimum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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 data package consists of a summary of the recommendations of the American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guideline on the Management of Hip Fractures in the Elderly.
This data package contains information about hospital readmission and deaths as well as hospital excess readmission reduction program. It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.
This data package contains reports on Adverse Events Total Hip Replacement for the year 2014, 2015, 2016, 2017 and 2018.
This trial dataset provides a detailed directory of 4,044 financial services in Chicago, including insurance agencies, banks, tax services, investment firms, mortgage lenders, and more. It contains 95 essential attributes such as business names, contact details, addresses, operating hours, and categories. Use this sample dataset to gain insights into the financial landscape of Chicago or build innovative financial solution POCs.
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.