Sold by: Mphasis
Deployed on AWS
An ML based solution for MTTR prediction of service tickets.
Overview
The MTTR (Mean Time to Resolution) predictor is an AI/ML based solution which predicts the time taken by a service agent to solve a specific ticket or an incident request. The solution learns the efficiency, experience and workload management metrics for various ticket types solved by service agents to arrive at the predictions. The solution helps business in optimal ticket allocation leading to a low MTTR, shorter wait time, fewer open incidents leading to improved efficiency and SLA (Service Level Agreement) adherence.
Highlights
- The solution uses a multi-factor approach and considers factors such as efficiency, experience, workload management for various ticket types for all incident managers etc. to predict MTTR for an incident.
- The solution incorporates learning from ticket management data from systems and predicts real time MTTR for an incident leading to improved ticket management metrics like fewer open incidents, shorter processing and wait times and improved SLA adherence.
- 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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $5.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $5.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $5.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $5.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $5.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $5.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $5.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $5.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $5.00 |
Dimensions summary
You pay by the hour for the compute instance that runs this model. Pricing splits into two usage modes: batch inference, which processes grouped data, and real-time inference, which serves live predictions. Within each mode, you choose from many AWS machine learning instance types. These range across general-purpose, compute-optimized, memory-optimized, and GPU-based families in varying sizes. Larger instances carry higher hourly rates. You scale cost by picking the instance that fits your workload and running it only for the hours you need. Real-time mode offers more instance choices than batch mode.
Top-of-mind questions for buyers
One HostHrs unit is one hour of running the model on a single chosen instance type. Billing counts each hour the instance stays active. Partial hours follow AWS metering rules. You pick one instance family and size, then pay its hourly rate for every hour it runs.
Charges apply per active host hour. A real-time endpoint accrues charges while it stays running, even between requests. Batch jobs charge only for the hours the instance processes data. Stopping the instance ends software charges, though underlying AWS storage fees may still apply.
Batch mode runs on an instance only while processing grouped data, so you pay for those job hours. Real-time mode keeps an endpoint running to serve live predictions, so charges continue for every hour it stays up. Batch suits scheduled workloads; real-time suits continuous prediction needs.
www.mphasis.com
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Vendor refund policy
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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
This is the third version of the algorithm
Additional details
Inputs
- Summary
- Request ID
- Request Resolved By
- Request Submitted Date and Time
- Request Priority
- Request Resolved Date and Time
- Request Category
- Request Status
- Limitations for input type
- No
- Input MIME type
- text/csv, application/zip
https://github.com/Mphasis-ML-Marketplace/Mphasis-ML-Marketplace-Mphasis-Optimize.AI-MTTR-Predictor
https://github.com/Mphasis-ML-Marketplace/Mphasis-ML-Marketplace-Mphasis-Optimize.AI-MTTR-Predictor
Resources
Vendor resources
Support
Vendor support
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