FOR THE WIN
PG Certificate in Artificial
Intelligence & Deep Learning
Online Instructor led 6 month
program

| Manipalprolearn.com3

| Manipalprolearn.com4

 Award from MAHE, an Institution of Eminence
 Real life case studies with real data from different domains (Marketing, Healthcare,
Media)
◦ Marketing data – Leads and conversion data from Manipal Global
◦ Audio scripts – for RNN and NLP
◦ CCTV footage – for object detection, face recognition
◦ Healthcare data – real x-ray images
 Highly hands on to prepare the learners to be job ready
 GPU based training environment
 Content delivery by industry experts/practitioners
 Strong Industry collaboration
5

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 Wikipedia
◦ “intelligence exhibited by machines”

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 "The application of a systematic, disciplined, quantifiable
approach to the development, operation, and maintenance
of software"—IEEE Standard Glossary of Software
Engineering Terminology. "an engineering discipline that is
concerned with all aspects of software production“
—Ian Sommerville.
(Source: Wikipedia)

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 Plain stress on ML/AI algorithms diverting attention from big picture
 Need for solid systems and software engineering principles for AI /ML
systems
 Need to develop skills for end to end process for understanding,
designing, building and evaluating AI/ML systems
 Tools for enhancing productivity of every aspect of engineering of AI/ML
systems needed
 Non Functional requirements like security are important and so are
additional needs like explainability

A computer program is said to learn from experience if its performance
at tasks improves with experience .
-Can be Unsupervised, or Supervised or Reinforcement

1. The myth of glorified data scientist – Only stress on Algorithms is a
bad idea
2. Spend time in Problem Identification – Use Design thinking for right
requirements – Get a domain guy for sure in the team
3. Spend a good amount of time in data acquisition and storage needs
(Without a robust big data infrastructure this is meaningless)
4. Spend countless hours on data schemas, data understanding and
data cleaning (Junk In Junk out)
5. Don’t ignore the architecture
6. Last but not the least there is a process (JIJO) – A robust data science
process is crucial much like CMM is root to success of IT services
success
Practical Issues in Building ML systems

1. Data acquisition
2. Data Understanding
3. Data Preparation
4. Hypothesis and modeling
5. Evaluation and Interpretation
6. Deployment
7. Optimization
Process: Follow Data Science Development Life Cycle – CRISP
DM Method for Machine Learning

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Google AutoML removing drudgery of navigating several hyperparameters for ML
IBM Research recently launched a framework for simplifying deep learning
programs authoring
End to End Pipelines are arising for automating ML processes like pipeline.ai
Like ML developer productivity deployment productivity is crucial example NicheAI
is working on simplifying deployments of AI ML workloads and optimizing costly
GPU needs
It is important to have a good tools for diagnosing ML programs – Like What if tool
from Google, LIME etc

 Today some domains are critical of AI/ML due to legislative compliance
issues
 Can we bring explainability to our models to help compliance
◦ LIME (Locally Interpretable Model Agnostic Explanations)
◦ Google ‘s What if Tool
◦ Guided backpropagation
 GDPR poses significant constraints on data acquisition for AI/ML
 Need to be able to visualize and explain models is becoming important
for business stakeholders

 Several innovative architectures like GPUs TPUs FPGA s bein
worked on
 Even Big Data Infrastructures like Spark are gearing to support
PySpark and SparkR
 However Performance optimization of programs for target
infrastructures still a far cry (Startups like NicheAI, PipelineAI )
 Cost is an important issue So Cost Effective GPU etc for target
customers becomes imperative, hence solutions emerging like
NimbleBox AI (Heroku for AI)
 Last but not least deployment productivity is turning out to be
a key innovation target just like development productivity

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Several examples of AI
technology of Computer
Vision with deep
learning to help in UxD
Google's AI Doodle
Bot enables
completion of low
fidel doodles.
Autodesk partners
with Airbus to enable
generative design of
airplanes
Examples of Deep
Learning based Vision
for Web Design
solutions emerging
GUI automation and
testing

 Sketch2CODE – Transforms HTML to code
 Nvidia Project Holodeck
 Autodesk Dreamcatcher
 Pixel2Code

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Root Cause Analysis –
Automation of root
cause analysis
Automated Defect
Prediction - Identifies
high-risk areas in the
application which
helps in risk-based
prioritization of
regression test cases
Test Prioritization
Smart Regression
Test Selection - Use
AI to match test cases
that need not be
retested

 Test.AI
 Moolya uses AI for testing
 Applitools does visual testing with AI based vision
 Appvance - User behavior based test case generation
 Testim.io – test case authoring

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Automated Concepts extraction from Requirements via NLP
•Actors identification
•Class Identification
•Use case extractor
Generating test cases from Textual Requirements
Design artifacts for Agile from User Stories

Detailed Malware analysis via ML
based pattern matching
Fuzz testing designed to find
vulnerabilities in software via ML
Security Risk understanding via
AI based decision support
system
Intrusion Prevention Systems

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Ticket Analytics to help cluster tickets
Automating RCA via Machine Learning
Text Mining of support tickets for clustering
End to End automation of simple / rule based processes
Bayesian Network based intelligent automation of infrastructure support
Data Centre Automation
Self Service Infra Process
Automated Help Desk Resolution
Process Gap Analysis via Analytics

https://www2.warwick.ac.uk/fac/sci/wmg/research/pard/pardprojects/electricalintegration/results/bayesian_diagnostics.pdf

 Important to look at broader software and systems
engineering issues for AI / ML systems
 Important to look at data as an important asset in software
engineering
 Look for opportunities for automation and process
improvement in Software engineering with AI/ML/Analytics

Thank you
Srinivas@tarahtech.com
For Manipal PGD AI DL
premjith.alampilly@manipal
global.com
Balu.nair@manipalglobal.co
m