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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Mphasis Optimize.AI Expert Identifier

By: Mphasis Latest Version: 2.4

The solution helps identify the right expert to be assigned for a new service request.

Product Overview

Expert Identifier is machine learning based model that uses information present in any incident/ticket management data such as: Ticket ID, Ticket Solver Id, Ticket Priority, Ticket Category, Ticket Submission and Resolved date and identifies the right expert to be assigned to a specific ticket or incident request. It can optimise ticket allocation, decreases the ticket resolution time and improve KPIs (Key Performance Indicators) such as customer satisfaction, adherence to SLA (Service Level Agreement), MTTR (Mean Time to Resolve), cost to company, etc.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

Supported content types: text/csv

The solution requires the user to provide input as .csv file with following data fields. It uses historical request resolution data to derive service provider's efficiency across request workloads and assign experts for new requests based on historical behaviour:

a. Request ID: Unique identifier for the request- alphanumeric e.g. SRV101_254859
b. Request Submitted Date and Time: The data and time when the request was submitted (Preferred format:YYYY-MM-DD HH:MM:SS)
c. Request Priority: Priority of the request e.g. High, Medium, Low
d. Request Resolved Date and Time: The date and time when the request was closed (Only for closed requests, Preferred format:YYYY-MM-DD HH:MM:SS)
e. Request Category: Type of request e.g. "Authentication issue","Server failure issue","Access grant request"
f. Request Status: Status of the request e.g. Open/Closed
g. Assigned/Unassigned: whether request is assigned to resolver (Assigned) or not (Unassigned)
h. Request Resolved By: Service Provider ID/name who resolved the ticket (Only for closed and unassigned tickets)

NOTE:

  • For Assigned requests, all the above data fields are mandatory("Request resolved by" should NOT be blank).
  • For Unassigned requests, all the above data fields except "Request Resolved Date and Time" and "Request Resolved By" are mandatory.
  • Provide a minimum of 10000 records (of assigned requests) for better results

Output

Output Content type: text/csv Output file: | Request ID | Request Status | Recommended Expert | |-------------|---------------|----------------| | 12426 | Open | zk8m6s8 |

For open requests with unassigned experts (i.e. unassigned requests), the solution will provide the "expert" to be allocated for each request. Please refer to "Recommended Expert" column for the expert assigned by the solution

Resouces

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

Mphasis Optimize.AI Expert Identifier

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

Customer Reviews