Yogesh Padmanaban
Specialises In (based on submitted proposals)
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keyboard_arrow_downHybrid Classification Model with Topic Modelling and LSTM Text Classifier to identify key drivers behind Incident Volume
45 Mins
Case Study
Intermediate
Incident volume reduction is one of the top priorities for any large-scale service organization along with timely resolution of incidents within the specified SLA parameters. AI and Machine learning solutions can help IT service desk manage the Incident influx as well as resolution cost by
- Identifying major topics from incident description and planning resource allocation and skill-sets accordingly
- Producing knowledge articles and resolution summary of similar incidents raised earlier
- Analyzing Root Causes of incidents and introducing processes and automation framework to predict and resolve them proactively
We will look at different approaches to combine standard document clustering algorithms such as Latent Dirichlet Allocation (LDA) and K-mean clustering on doc2vec along-with Text classification to produce easily interpret-able document clusters with semantically coherent/ text representation that helped IT operations of a large FMCG client identify key drivers/topics contributing towards incident volume and take necessary action on it.
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