In this presentation we talked about how macnica.ai is preparing to provide AI as solutions to Japanese enterprises and business.

1. Welcome to 2nd AI Expo, Tokyo Japan
April 4th ,5th , & 6th , 2018 at Tokyo Big Sight

2. Macnica AI (Global)
Your partner in Global AI Revolution

3. My Journey
• Avkash Chauhan
• Currently:
• Email: avkash@macnica.com
• Macnica, VP, AI Platforms and Business (Recently)
• Previously
• H2O, VP Enterprise Products and Customers (2016-2018)
• Top 225 fortune 500 companies, 20+ Billion $$ enterprises
• Big Data Perspective, Founder and Principal (2014-2016)
• Big Data Appliance using Auto Encoders with Deep Learning
• Microsoft (2005-2013)
• HDInsight, Windows Azure, Windows CE/Mobile
• ISR (2001-2004) – Tokyo and San Francisco, USA
• NTT Comware & Docomo, Hitachi, NEC, KDDI, NISSAN

4. My Introduction
2016-2018 2014-2016 2013-2014 2005-2013 2001-2005
VP,
Enterprise Products
& Customers
Founder & Principal Software Architect
Engineer & Developer
Azure, HDInsight,
Windows CE Team
Software
Engineer
NTT Comware
NTT Docomo,
Hitachi, NEC,
KDDI, NISSAN
Top Fortune 500
Customers
Japanese Electronics
Giants
Financial & Govt
Large Scale Data
Cloudera,
Hortonworks,
MapR, Ninja
Oracle, FireEye
Top 225 fortune
500 companies,
20+ Billion $$
enterprises
2018….
VP,
AI Platforms &
Business
PLATFORA

5. Industry Experience
Retail
Healthcare
Marketing
Finance
Insurance
Telecom
IT/Software
IoT/IIoT

6. H2O Open Source ML Engine Adoption
Today: January 2018

7. Thinking Data as products
• Using Data to build Model
• Using Models for every actionable
intelligence
• Using Models of every rule based
transection

8. Model is new currency!!
Model
=
Data Data Data
Data Data Data
Data Data Data

9. Why AI?
Major
Cost
Saving
Business
Process
Optimization
New
Line of
Business

10. How to make AI happen?
Complexity to develop Time to developLow
Hard
Cost to develop
Cost Saving
BPO
New Business

11. Machine Learning Life Cycle
Machine
Learning
Cycle

12. Scalable
Machine
Learning
A system of Automated AI for any Business
Data Ingest
Automated
Feature
Engineering
Model
Data Data Data
Data
Visualization
Data Data Data
Data Data Data
Machine
Learning
Interpretability
Scoring
Package
Genetic
Algorithm
ML/AI
Algorithm
65
4
7
3
2
1
8

13. Machine Learning Development Cycle
Data Science + Software Engineering
(80/20)
Software Engineering
Data Engineering
Blobs
S3
NFS
Data
Data
Data
HDFS
RDBMS
Code
Stream
Hive
LFS
Building Machine Learning Applying Machine Learning

14. Ensembles
Data Science + Software Engineering
(80/20)
Software Engineering
Building Machine Learning Applying Machine Learning
Machine Learning Development Cycle
Blobs
S3
NFS
Data
Data
Data
HDFS
RDBMS
Code
Stream
Hive
LFS Model Deployment
Smartphone Watch
T-Shirt
Engine
Predictive Maintenance
Consume
r
Industrial
Web
Factory
Enterpris
e
Web Analytics Internal
Process
Linear
Modeling
Tree Based
Modeling
Neural
Networks
• H2O
• Scikit
• SVM
• R Package
• CoxPH
• H2O
• Scikit
• R Package
• XGBoost
• LightGBM
• TensorFlow
• mxnet
• Torch
• CNTK
• Chainer
Open Source ML/NN Engines
Linear
Modeling
Tree Based
Modeling
Neural
Networks
• H2O
• Scikit
• SVM
• R Package
• CoxPH
• H2O
• Scikit
• R Package
• XGBoost
• LightGBM
• TensorFlow
• mxnet
• Torch
• CNTK
• Chainer
Open Source ML/NN Engines
• AutoML
• DriverlessAI

15. Rapid Prototyping in Machine Learning
Data Data Data
MODEL

16. Rapid Prototyping in Machine Learning
Data
Transformation
(Feature Engineering)
Model building
& evaluation
Data Data Data
MODEL

17. Business ready AI (Industry Regulations)
Machine Learning
Interpretability
Partial Dependency Plots
Variable Importance
Decision Trees
LIME/K-LIME

18. Business Ready AI Results (After Rapid Prototyping)

19. AI Adoption strategy for any organization
100 %
Novice in
DS/AI
50 %
80-20 ratio between Data Science
& Software Engineering
25-75 ratio between Data Science
& Software Engineering
Expert in
DS/AI
50-50
expertise in
DS/AI
0 %
Identify, where you are?
Having a partner will help your journey?

20. Thanks!!
Please do reach out to me (avkash@macnica.com) or Macnica for
any question on this regard!!