The document provides an overview of machine learning and artificial intelligence concepts. It discusses: 1. The machine learning pipeline, including data collection, preprocessing, model training and validation, and deployment. Common machine learning algorithms like decision trees, neural networks, and clustering are also introduced. 2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined. 3. The impact of AI on society and the workplace. While AI is predicted to help humans solve problems, some people remain wary of technologies like home health diagnostics or AI-powered education. Responsible development of explainable AI is important.

1. zekeLabs
AI - Hype vs Reality
Learning made Simpler!
www.zekeLabs.com

2. Modules
● Machine Learning Ecosystem
● AI In a Nutshell
● AI adoption across different domains
● Identify right tools
● Building AI Team

3. Module 1
● Black box Introduction to Machine Learning
● Machine Learning Pipeline
● Adopting Machine Learning in your Product : Use cases
Machine
Learning
Ecosystem

4. Black Box Introduction to ML

5. What is not Machine Learning ?
● Rule Based Approach
● Legacy Systems

6. Learning
Algorithm
What is Machine Learning ?
● Solve prediction problem
● Logic is learned from examples & not by rules
Prediction Function
or
Trained Model
Training Data
Input Data
Prediction

7. Types of Machine Learning
Machine Learning
ReinforcementUnsupervisedSupervised
Task Driven Data Driven Environment Driven

8. Spam Mail Detection
● Input - Mail
● Output - Spam or Ham
● Supervised Machine Learning
● Binary Classification Problem

9. ● Input - Sensor Data
● Output - Failure time
● Supervised Machine Learning
● Regression Problem
Predicting Lift Failure

10. ● Input - Accident details
● Output - Insurance amount
● Supervised Machine Learning
● Regression Problem
Predicting Insurance Amount

11. ● Input - Patient Synopsis (fever,
temperature, BP, etc. )
● Output - Diagnosis
● Supervised Machine Learning,
● Multi-class classification Problem
Medical Diagnosis

12. Q. What is common between them ?

13. Market Segmentation
● Input - Customer Details
● Output - Clusters
● Unsupervised Machine Learning
● Clustering Problem

14. Robot playing Football
● Input - Player information, Rewards
● Output - Action to score
● Reinforcement Learning

15. Machine Learning Pipeline

16. Machine Learning Pipeline (MLP)

17. MLP - Business Understanding
● Business understanding includes clarity what you are trying to achieve.
● Machine learning is not possible with small data size
● Consolidating data pipeline to channelize continues flow of data.
● Web scraping, data lakes access, REST etc.

18. MLP - Data Wrangling
● Production data is never clean.
● It needs a major effort ( around 70% of total effort ) to make it ready for next stage
● Transforming & mapping data from raw format to another format ready for next stage

19. MLP - Data Visualization
● Visualization makes it easy to grasp difficult concepts
● Find useful pattern in the data
● Interactively drill down into charts for deeper details

20. Vectors - Fixed length array of numbers
● Text documents
● Image files
● CSV
● Audio
● Video
● Time Series data
● Many more ...
MLP - Data Preprocessing
Feature Extraction

21. MLP - Model Training
Learning Algorithm
Regression/Trees/SVM/Naiv
e Bayes/Neural Networks/
Prediction Function
or
Trained Model

22. ● Linear Regression
● Logistic Regression
● Naive Bayes
● Nearest Neighbors
● Decision Trees
● Ensemble Methods
● Clustering
● Support Vector Machines
● Neural Networks
● CNN
● RNN
● GAN
MLP - Learning Algorithms

23. Prediction
Prediction Function
or
Trained Model

24. MLP - Model Validation
● Training different learning method will give you different trained model.
● Also, each model have huge possibilities of configuration (hyper-parameters).
● Finding the best model among all possibilities & best configuration for it is done as a part
of Model Validation.
● If results are not satisfactory, one has to go back in the chain & fix a few things

25. MLP - Deployment
Trained Model
Or
Interface Model
Consumers RESTful Interface

26. Adopting Machine Learning
Real Stories

27. 1. Customer Service Industry

28. 1. Reduce manual
effort of classifying
reviews.
2.Channelizing data
from Web server to
Analytics Engine.
1. Getting
data ready for
visualization.
2. Historical
data shows
past trends.
Visualization
of trend
Text needs to
be tokenized
& vectorized
Different
models were
trained.
Naive Bayes,
SGD Classifier
Choose the
best model
with best
hyper-
parameter
Naive Bayes
(MultinomialNB)
was chosen & put
in deployment
1. Customer Service Industry
● Manually labeled data is used for training model.
● Labels are target & review are feature data
● Batch training is supported by MultinomialNB allowing incremental learning
● Any mis-classification done by model will be labelled right & fed again

29. 2. Fast Query Chatbots

30. 2. Fast Query Chatbots
1. Reduce manual effort
understanding the text
query
2. Waiting for BI has a
long turnaround time
3. We are trying to do this
using chatbot
1. Getting data
ready for
visualization.
2. Historical
data shows
past trends
Visualization
of trend of
text & sql
Text cannot
be used for
ML
Needs to be
tokenized &
vectorized
Deep learning
models with
different layer
configuration
Choosing the
best model
with best
hyper-
parameter
Model with best
config was chosen
& put in
deployment
● Convert natural language query to SQL Query
● Model is trained with historical text (feature) & SQL (target)
● The generated SQL was executed & Output was subjected to visualization libraries
● Anybody without database & infra understanding can get visualization in seconds

31. 3. Preventing System Failure

32. ● Deep Learning - A specialization of Machine Learning
● ML vs DL vs AI
● AI Timeline
● What does AI consist of ?
● Where AI can be adopted in business
● Challenges in adopting AI
Module 2
AI in a
Nutshell

33. What is Deep Learning ?
● Specialized Learning Technique
● Rather than we choosing features for learning, this technique finds important
feature derivatives.
● Objective is to learn best derived features for prediction.
● It mimics the way our brain learns
● Very useful for natural language, computer vision, audio, video etc.

34. Do you always need Deep Learning ?
● More data is required for Deep Learning
● More Compute Power
● Models less interpretable
“Don’t kill a mosquito with a cannon ball”
Don’t use Deep Learning if you don’t need to

35. ML vs DL vs AI. Timeline

36. What does AI consist of ?

37. Components
of any
AI product

38. Where AI got into in business?

39. Imp : Advice to executives about AI
● Everybody should embrace modern capability of AI, on other they should also think
about business specific problems. Not every single tool that AI community can
develop can suit them correctly.
● Biggest challenge is people change not technology change, biggest gap now is
people who can map technology to business problem.
● Insourcing vs outsourcing. Building Team vs using enterprise solutions.
● AI will change everything in next few decades. Be a part of it.

40. Challenges - Data & Security
● Volume of data - Machine learning
on smaller data is infeasible.
● Accessibility of data - Important
data is not accessible & may be in
encrypted format.
info@zekeLabs.com | www.zekeLabs.com | +91

41. Compute, Storage & Network Power
● AI products needs data gathering from sensors, servers etc.
● Once gathered, data needs to be stored for further processing.
● Learning algorithms & data processing activities need lot of compute power.

42. Infrastructure for development
● Finding the best model is an iterative process.
● More experiments leads better model.
● Hyper-parameter Tuning
● Scaled infrastructure for developer is
important.

43. Infrastructure for deployment
● Speedy Deployment
● Easy deployment
● Fluctuating Demand
● Need of Elastic infrastructure
● Cost optimization

44. Summary of
Challenges

45. Cost optimization:
● Use Open Source alternatives
● Infrastructure optimization
● Don’t reinvent the wheel

47. ● Will AI benefit human ?
● AI in human computer interaction
● Impact of AI on business
● Impact on workplace
● Impact on society
Module 3
Impact of
AI

48. AI benefit human - social, environmental
● Predicting diseases
● 60% People would prefer AI assistance over humans as financial advisors or tax
preparers
● 71% people believe that AI will help humans solve complex problems and help live
more enriched lives

49. AI Assistants
● Saves Time
● Calendar events reminder
● Helps get things done

50. Impact of AI on business

51. More

52. AI advisor & manager at workplace

53. Impact on Decision Makers
● Adoption of AI advisors

54. What can be outsourced to AI assistant?

55. Impact of artificial intelligence on society
● People are averse to the idea of availing annual
health check-ups at home with a robotic smart kit
(77%) or having chatbot assistant teachers in
universities/ colleges that lower the cost of overall
tuition (61%).
● Responsible AI ensures that its workings are aligned
to ethical standards and social norms pertinent within
its scope of operations.
● Explainable AI is responsible for building AI models
with accountability and the ability to describe or
depict why a certain decision was made by the
algorithm.

56. ● Programming Language
● Open source libraries
● Infrastructure Optimizations
● Other alternatives
Module 4
Identify the
right tools

57. Choose the
Right
Programming
Language

58. Why Python makes life easy ?
● Easy to learn for ETL developers
● Integrates very well with other technologies
● Full-stack development -
○ Dashboard using bokeh,
○ Web application using django,
○ Machine learning models using scikit,
○ Scaling using PySpark

59. Choose appropriate Libraries
- Statistical Modeling & Data Processing

60. Choose appropriate Libraries
- Visualization

61. Choose appropriate Libraries
- Machine Learning or Deep Learning

62. Infrastructure Optimization
Monolithic or Serverless

63. Monolithic Infrastructure - Preallocated Infra
Model Training
● Developers request access
whenever required
● Might incur delay in peak
working hours.
● Idle in non-working hours
Model Interfacing
● Idle in non-peak hours.
● May fall short in spikes.
● Pay even if infra is not used

64. Serverless Infrastructure - Elastic Allocation
Model Training
● No-preallocation
● Pay only for what you use
● Absolute no idle time for infra
● No wait time for developers
Model Interfacing
● Allocate infra only when required
● Scales down during non-peak
hours
● Improved customer experience
even in peak hours

65. Serverless Infrastructure Solutions
● Open Function as a Service (OpenFaas)
● AWS Lambda
● Google Cloud Function
● Azure Function

66. Distributed Machine Learning using Spark
● Apache Spark is a distributed data processing
framework.
● Many machine learning algorithms are
implemented in Spark.
● Most of the API’s are same that of scikit-learn
● Scaled ETL & Machine Learning can be done
using Spark

67. Other alternatives
Google Cloud AI

68. ● Adoption of AI
● Skills
● Hiring or upskilling
● Upskilling workforce
Module 5
Build AI
Team

69. Adoption Strategy
Build Business Case Scale Efficiently
Create Data
Driven Culture

70. Skills

71. Talent Acquisition
● Upskill your current team ?

72. Upskilling workforce
● It’s possible to make use of the people who have delivered for you in the past.

73. Q & A

74. Repositories
● https://github.com/zekelabs/machine-learning-for-beginners
● https://github.com/zekelabs/tensorflow-tutorial/
● Dog breed prediction -
https://www.edyoda.com/resources/watch/54AEA4CDC35394F1183A9D
D17AA47/
● Python learning course -
https://www.edyoda.com/resources/videolisting/98/
● Learning Path - https://www.edyoda.com/program/data-scientist-program

75. Visit : www.zekeLabs.com for more details
THANK YOU
Let us know how can we help your organization to Upskill the
employees to stay updated in the ever-evolving IT Industry.
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