ITSM neural network to classify incidents with historical training dataset.

1. Build a Neural Network for
IT Self-Service with
TensorFlow
Ashish Kumar
Twitter: @ashish_fagna
LinkedIn: https://www.linkedin.com/in/ashkmr1/
ashish.fagna@gmail.com

2. What is Machine learning
?
• Machine learning is a field of computer science that
gives computers the ability to learn without being explicitly
programmed.
• Machine learning algorithms are techniques for estimating the
target function (f) to predict the output variable (Y) given input
variables (X).

3. Why Machine Learning
Matters ?

4. Neurons :
Biological and Artificial
• Neurons (nerve cells) are basic elements of the nervous
system.
Biological Neuron Artificial Neuron

5. AlphaGo

6. OpenAI wins Dota 2
YouTube

7. ML is subset of AI
Source : https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12

8. Machine Learning Basics

9. • Neural networks, is a programming
paradigm which enables a computer
to learn from observational data.
• Deep learning, a powerful set of
techniques for learning in neural
networks,
• Provides the best solutions to many
problems in image recognition,
speech recognition, and natural
language processing.
Neural Network

10. Neural Network
• Artificial Neural Networks (ANN) are
inspired by biological nervous systems.
• It is composed of a large number of
neurons (highly inter-connected
processing elements).
• An ANN is configured for a specific
application, such as pattern recognition
or data classification

11. Neural Network - Layers
• The processing layers are the hidden layers.
• These hidden layers perform specific tasks on the incoming data
and pass on the output generated by them to the next layer.
• The input and output layers are the ones visible to us, while are
the intermediate layers are hidden.

12. Types of Neural
Networks
source: http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

13. Types of Neural
Networks
source: http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

14. Learning Paradigms
1. Supervised Learning
2. UnsuperVised Learning
3. Reinforcement Learning

15. Supervised Learning
• Supervised Learning is a type of system in which both input
and desired output data are provided.
• Use cases : Chatbots, self-driving cars, facial recognition
programs and robots.
source: https://www.coursera.org/learn/machine-learning/lecture/1VkCb/supervised-learning

16. Supervised Learning
• MRI scans used in NeuroScience Research.

17. Unsupervised Learning
• Identify the patterns and trends from given datasets.
• An AI system is presented with unlabeled, uncategorised
data and the system’s algorithms act on the data without
prior training.
• The output is dependent upon the coded algorithms.
• Example : Natural grouping of Xs (X= human languages,
stocks, gene sequences, animal species)

18. Unsupervised Learning
• used in neuroscience research to find relationships amongst
the data
Clustering Algorithm
source: https://www.youtube.com/watch?v=QEuGkcpa1Tw

19. Supervised Learning ~
Approximation
Unsupervised learning ~ Description

20. Reinforcement Learning
• Machine’s learn its behaviour based on feedback from the
environment.
• Example: A bot improves playing a tic- tac -toe game by
playing it and learning from experience.
source: https://www.quora.com/What-is-reinforcement-learning

21. What problems can
neural nets solve for IT?
• Predict the assignment group for an incident
• Use NLP and NLU to diagnose employee issues from a
virtual agent
• Determine which CI caused a problem
• Specify the risk of implementing a change

22. Popular Deep Learning Frameworks
•TensorFlow
•Caffe2
•PyTorch
•Apache MXNet
•Microsoft Cognitive Toolkit
•Keras
•Torch
•Deeplearning4j
•Chainer
•Theano, Lasagn
Open Neural Network Exchange (ONNX), a
platform for inter operability of models (CNN,
RNN) between various DL Frameworks

23. Tensor ?
• Tensor can be thought of a
multi dimensional array of
numbers.
• Tensors often offer more
natural representations of
data,
• e.g., consider video, which
consists of obviously
correlated images over time.
We can turn this into a matrix

24. TensorFlow
• Originally developed by the Google
Brain Team within Google's Machine
Intelligence research organisation.
• TensorFlow provides primitives for
defining functions on tensors and
automatically computing their
derivatives.
• An open source software library for
numerical computation using data flow
graphs.

25. TensorFlow: Stats
• 12,000+ commits since Nov, 2015
• 570+ contributors
• 1M+ binary downloads
• 5000+ TensorFlow related repositories on GitHub
• #15 most popular repository on GitHub by stars - across
all categories
• Used in ML classes at many universities: Toronto,
Berkeley, Stanford,

26. Data Flow Graph ?
Computations are represented as graphs:
• Nodes are the operations (ops)
• Edges are the Tensors (multidimensional
arrays)
Typical program consists of 2 phases:
• construction phase: assembling a graph
(model)
• execution phase: pushing data through
the graph

27. TensorFlow
Architecture

28. Machine Learning Basic
Concept

29. Neural Network
Simulation
Tinker with a Neural Network in Your Browser:
Link: playground.tensorflow.org

30. Image Classification

31. Datasets :
Image Classification
source: https://martin-thoma.com/sota/
Dataset Year Score Type Paper
ImageNet 2012 2015 3.08% Top-5 error - [SIVA16]
MNIST 2013 0.21% error - [WZZ+13]
CIFAR-10 2017 2.72% error - [G17]
CIFAR-100 2016 15.85% error - [G17]
STL-10 2017 78.66% accuracy + [Tho17-2]
SVHN 2016 1.54% error - [ZK16]
Caltech-101 2014 91.4% accuracy + [HZRS14]
Caltech-256 2014 74.2% accuracy + [ZF14]

32. Image Classification

33. Convolution Neural Network
(CNN)
source: https://www.kernix.com/blog/a-toy-convolutional-neural-network-for-image-classification-with-keras_p14

34. Activation Functions
• Sigmoid
• ReLU (Rectified Linear Units)
• SoftMax

35. Handwritten Digit (MNIST) :
SoftMax model
https://github.com/Kulbear/deep-learning-nano-foundation/wiki/ReLU-and-Softmax-Activation-Fu

36. Handwritten Digit (MNIST) : SoftMax
model

37. Matrix Notation:
100 Images Matrix (matmul) Weight
matrix

38. softmax on a bunch of images
TensorFlow Equivalent
source: https://docs.google.com/presentation/d/1TVixw6ItiZ8igjp6U17tcgoFrLSaHWQmMOwjlgQY9co/pub?slide=id.g110257a6da_0_476

39. TensorFlow Code: Handwritten Digit (MNIST)
Classification
https://docs.google.com/presentation/d/1TVixw6ItiZ8igjp6U17tcgoFrLSaHWQmMOwjlgQY9co/pub?slide=id.g110257a6da_0_59

40. Model training on
ServiceNow
Demo 1

41. Text Classification

42. ITSM Incidents - Training JSON Data
{
"Service Desk": [
"Can't read email",
"Forgot email password",
"Reset my password",
"How do I create a sub-folder"
],
"Network": [
"Unable to get to network file shares",
"change to my w2",
"Lost connection to the wireless network",
"Issue with networking",
"Trouble getting to Oregon mail server"
],
"Hardware": [
"CPU load high for over 10 minutes",
"Printer in my office is out of toner",
"Need new Blackberry setup",
"Rain is leaking on main DNS Server",
"Seem to have an issue with my hard drive”
],
"Software": [
"SAP Financial Accounting application appears to be down",
"The SAP HR application is not accessible",
"Hang when trying to print VISIO document",
"EMAIL is slow when an attachment is involved",
"address correction on w2”

43. Demo 2
1. Prepare Data
2. Neural Network :
• Model training using historic IT incidents
• Auto-route an incident to correct assignment group
source: : https://sourcedexter.com/tensorflow-text-classification-python/

44. Why TensorFlow is popular
v/s alternatives
• The visualisation module (TensorBoard): One of the
main lacking areas of almost all open source Machine
Learning packages, was the ability to visually model and
follow the computation pipeline.
• The all-in-one hardware implementation approach: The
libraries of TensorFlow can be deployed in all kinds of
hardware, from mobile devices to more powerful
heterogeneous computing setups.

45. Questions

46. Twitter: @ashish_fagna
LinkedIn: https://www.linkedin.com/in/ashkmr1/
ashish.fagna@gmail.com
Thank You !