Hybrid Classification Model with Topic Modelling and LSTM Text Classifier to identify key drivers behind Incident Volume

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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Outline/Structure of the Case Study

Primary Speaker: Shalini Sinha

1. Baseline Text Classification solution for Incident Root Cause Identification

2. Challenges with Manual labelling of large scale text data - Sampling error, Ambiguity in Manual labelling, Labor-intensive

3. Topic Modelling with Latent Dirichlet Allocation to cluster Incidents

4. Limitations of Topic Modelling Techniques - Domain specific Stop-words, Semi Structure in Incident Description, Topic to word distribution not logical representation of topic description

5. Hybrid Classifier with Distance based Sampling

6. Auto label mapping for volume training data generation

7. LSTM Model for Incident Classification

8. Question and Answers on Algorithm, hyper-parameter tuning, etc - Yogesh P, Ashok

Learning Outcome

1. NLP: Challenges of Manual sampling of large scale Text data and how to handle it

2. NLP: LDA Topic Modelling pro and con with data examples

3. NLP: Text processing issues encountered while processing real-life Enterprise data and how to handle it

4. Unsolved problems like parsing of structure in the text that create unnecessary Topic with high Topic Match Probability - Open discussion

Target Audience

Data Scientist working on NLP specially Topic Modelling problem

Prerequisites for Attendees

Basic Understanding of NLP specially Topic Modelling

Supervised vs Unsupervised Learning

schedule Submitted 1 month ago

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      Knowledge Graphs

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      Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

      There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

      This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

    • Liked Shrutika Poyrekar
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      Shrutika Poyrekar / kiran karkera / Usha Rengaraju - Introduction to Bayesian Networks

      90 Mins
      Workshop
      Advanced

      { This is a handson workshop . The use case is Traffic analysis . }

      Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

      In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

    • Liked Saurabh Jha
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      Saurabh Jha / Usha Rengaraju - Hands on Deep Learning for Computer Vision

      480 Mins
      Workshop
      Intermediate

      Computer Vision has lots of applications including medical imaging, autonomous vehicles, industrial inspection and augmented reality. Use of Deep Learning for computer Vision can be categorized into multiple categories for both images and videos – Classification, detection, segmentation & generation.

      Having worked in Deep Learning with a focus on Computer Vision have come across various challenges and learned best practices over a period experimenting with cutting edge ideas. This workshop is for Data Scientists & Computer Vision Engineers whose focus is deep learning. We will cover state of the art architectures for Image Classification, Segmentation and practical tips & tricks to train a deep neural network models. It will be hands on session where every concepts will be introduced through python code and our choice of deep learning framework will be PyTorch v1.0.

      The workshop takes a structured approach. First it covers basic techniques in image processing and python for handling images and building Pytorch data loaders. Then we introduce how to build image classifier followed by how segmentation was done in pre CNN era and cover clustering techniques for segmentation. Start with basics of neural networks and introduce Convolutional neural networks and cover advanced architecture – Resnet. Introduce the idea of Fully Convolutional Paper and it’s impact on Semantic Segmentation. Cover latest semantic segmentation architecture with code and basics of scene text understanding in pytorch with how to run carefully designed experiments using callbacks, hooks. Introduce discriminative learning rate and mixed precision to train deep neural network models. Idea is to bridge the gap between theory and practice and teach how to run practical experiments and tune deep learning based systems by covering tricks introduced in various research papers. Discuss in-depth on the interaction between batchnorm, weight decay and learning rate.

    • Liked Ramanathan R
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      Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

      480 Mins
      Workshop
      Intermediate

      “Time is precious so is Time Series Analysis”

      Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

      Structure of the workshop goes like this

      • Basics of time series analysis
      • Understanding Time series data with pandas
      • Preprocessing Time Series data
      • Classical Time series models (AR, MA, ARMA, ARIMA, SARIMA, GARCH, E-GARCH)
      • Forecasting with MLP (Multi-Layer Perceptron)
      • Forecasting with RNN (Recurrent Neural Network)
      • Forecasting with LSTM (Long Short Term Memory Network)
      • Understanding Financial Time Series data and forecasting with RNN and LSTM
      • Boosting techniques in Time series data
      • Developing intuition to choose the right network.
      • Dealing with large scale Time Series data



      Libraries Used:

      • Keras (with Tensorflow backend)
      • matplotlib
      • pandas
      • statsmodels
      • prophet
      • pyflux
      • tsfresh