Sold by: Mphasis
Deployed on AWS
The solution helps identify the right expert to be assigned for a new service request.
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.
Highlights
- The solution is based on a multi-factor model which considers: 1. Request Category and priority 2. Service Provider's Experience, Expertise and Efficiency across Workloads (Service Provider Queue)
- The solution automatically incorporates the evolving service provider behaviour by constantly updating the model to update the provider’s efficiency. It is process agnostic and allows for customisation by providing training and predictions on client specific data.
- Mphasis Optimize.AI is an AI-centric process analysis and optimization tool that uses AI/ML techniques to mine the event logs to deliver business insights. 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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Dimensions summary
You pay by the hour for the compute instance that runs this AI process-mining engine. Pricing splits into two modes: Batch inference, for processing data in scheduled runs, and Real-Time inference, for on-demand responses. Within each mode, you choose an AWS SageMaker instance type. Options range from small, general-purpose and compute-optimized instances up to memory-optimized and GPU-accelerated instances. Larger instances with more compute, memory, or GPU power cost more per hour. You are billed only for the hours each instance runs, with no upfront commitment.
Top-of-mind questions for buyers
HostHrs meters each hour a chosen SageMaker instance runs the process-mining engine. You pay per instance-hour of active runtime. Billing tracks running time on the specific instance type you select, so cost depends on how long the instance stays active.
Batch inference charges per hour while the instance processes scheduled data runs, then stops. Real-Time inference charges per hour while an endpoint stays available for on-demand requests. Batch suits periodic jobs. Real-Time suits continuous availability, which keeps the instance running and accruing hours longer.
Charges accrue per hour only while an instance runs. Batch instances stop accruing once the job finishes. Real-Time endpoints keep accruing as long as they stay available, even without active requests. Stop or delete endpoints to end software charges. Underlying AWS storage fees may still apply.
www.mphasis.com
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Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
Updated version with new features
Additional details
Inputs
- Summary
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.
- Limitations for input type
- * For **Assigned** requests, all the above data fields are mandatory. * 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
- Input MIME type
- text/csv , text/plain
https://github.com/Mphasis-ML-Marketplace/Mphasis-Optimize.AI-Expert-Identifier
https://github.com/Mphasis-ML-Marketplace/Mphasis-Optimize.AI-Expert-Identifier
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Request ID | Unique identifier for the request- alphanumeric e.g. SRV101_254859 | Type: FreeText | Yes |
Request Submitted Date and Time | The data and time when the request was submitted (Preferred format:YYYY-MM-DD HH:MM:SS) | Type: FreeText | Yes |
Request Priority | Priority of the request e.g. High, Medium, Low | Type: Categorical
Allowed values: High, Medium, Low | Yes |
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) | Type: FreeText | Yes |
Request Category | Type of request e.g. "Authentication issue","Server failure issue","Access grant request" | Type: FreeText | Yes |
Request Status | Status of the request e.g. Open/Closed | Type: Categorical
Allowed values: Open/Closed | Yes |
Assigned/Unassigned | whether request is assigned to resolver (Assigned) or not (Unassigned) | Type: FreeText | Yes |
Request Resolved By | Service Provider ID/name who resolved the ticket (Only for closed and unassigned tickets) | Type: FreeText | Yes |
Resources
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Support
Vendor support
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