AI(Artificial Intelligence) is poised to transform every business industry domain as is now anticipated to be the 4 th revolution that has potential to change the way interact, conduct business, offer richer solutions with continuous refinement at scale and depth. Between now and end of 2030, it is estimated to create $13 trillion of GDP growth in business and growth and so enterprises and governments alike are now laying their AI strategy and mission critical statements for future. Raja will be sharing strategy and structure for: what does it takes to lay AI strategy and build AI products and solutions; what are the tenants for a successful AI project – pilot to production; common pitfalls and recommendations for AI projects; technology landscape – Classical ML to Deep Learning(Neural Networks), etc. Raja Chandra Rangineni is a seasoned technologist and hands-on leader known for delivering innovative, data-driven strategies and tactical roadmaps as the core of high- performance commercial solutions in areas of Data Analytics, Enterprise Architecture, Big Data, Cloud, Data Science and Blockchain for global enterprises. He collaborates with executive leadership to articulate vision and chart the course for investment in open source frameworks and services with blueprints and reference architectures. Provide thought leadership and architect models that consistently achieve user acceptance across the business community.

2. About Me

3. Agenda
1. About Me
2. Big Bang and Evolution
3. AI - ML - DL
 Artificial Intelligence
 Machine Learning
 Deep Learning (ANN)
4. AI Strategy & Structure
 Setting up your project for success
 Trademarks of a strong pilot project
5. Implementation - Pitfalls
 Mission
 Data/Process
 Technology
 People

4. Big Bang and Evolution…..
Image Source: https://blogs.nvidia.com/

5. Artificial Intelligence – Branches

6. Machine Learning - Branches
“Algorithms that parse data, learn from that data, and then apply what they’ve learned to make
informed decisions” .

7. Deep Learning(Artificial Neural Networks - ANN)
Image Source: https://wcarvalho.github.io/review/2017/12/23/building_machines_glossary/

8. Setting up your project for success
#1. Execute pilot projects to gain momentum

9. Trademarks of a Strong AI Pilot Project

10. Setting up your project for success
#2. Build an internal AI team

11. Internal AI team - Responsibilities

12. Setting up your project for success
#3. Provide broad AI training

13. Training - Strategy

14. Setting up your project for success
#4 Develop an AI strategy
(or as #1 step?)

15. AI Strategy - Coherent

16. AI Product Strategy – Data Acquisition

17. Implementation - Pitfalls
#1. No Mission Statement

18. Machine Learning Mission Statement
This initiative help grow our business by [how the ML project
generates ROI]. In order to complete this project, we will need data
from [insert group(s) name(s)]. This data is stored across [one or
more system(s)]. Once we have accessed the data, we will need help
from [insert group(s) name(s)] to verify that this data is true and
accurate. Machine learning expertise will be applied by [insert
group(s) name(s)]. After multiple iterations, the project is successful
when[insert success metrics].
Don’t Be A Teenager
Partner Source

19. Roles and Responsibilities

20. Pitfall #2 – You Don’t Know Your Data
Implementation - Pitfalls

21. Your Data = One State = Clean

22. Pitfall #3 - You Don’t Have a Technology Strategy
Implementation - Pitfalls

23. AI Solution – Life Cycle

24. Inference Units – Platform - Landscape

25. AI/ML Frameworks – Vendors – Notebooks
https://www.kdnuggets.com/2019/02/gartner-2019-mq-data-science-machine-learning-changes.html

26. Cloud OR On-prem

27. Pitfall #4 - You Don’t Have a People Strategy
Implementation - Pitfalls

28. People