Sold by: MphasisÂ
Deployed on AWS
An NLP based system to classify IT incidents/tickets based on ticket description, using an attention based model.
Overview
This solution analyzes textual description of incidents from ticket data to predict its category. It uses an attention based pretrained model to classify the tickets over a predefined set of incident categories using these textual descriptions. These incident categories along with the respective tickets are provided as output which can be used by an IT service management tool. This reduces the efforts required by L0 associates to read through the description and assign the categories.
Highlights
- A Deep Learning solution with Transformer based learners predicts the incident category of tickets based on the description provided by the user. The prediction engine comprises a pre-trained model to predict the incident category of the tickets. This requires no training and the models are pre-built.
- The set of incident categories used by the Transformer learners are identified from the textual description using NLP based ML approaches. These data driven categories are prominent topics that have generalized coverage (relevance) spanning the IT ticket data of various business verticals.
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Details
Sold by
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.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge 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 |
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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Vendor terms and conditions
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Content disclaimer
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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
It is the third version of the algorithm.
Additional details
Inputs
- Summary
The model requires a CSV file as input. It is mandatory to include following columns.
- Incident Number: Unique ID for each incident
- Description: Textual data that explains the incident.
- First column in the dataset should contain Incident Number and second column should contain Description.
- Max number of input data points -100.
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/IT-Incident-Category-Prediction/blob/main/Input/Input.csv
https://github.com/Mphasis-ML-Marketplace/IT-Incident-Category-Prediction/blob/main/Input/Input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Incident Number | This column contains unique ID for each incident. | Type: FreeText | Yes |
Description | This column contains textual data that explains the incident. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
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