기업은 역사상 가장 급격한 기술 변화 시기에 진입했으며 인공 지능(AI)은 전체 인력의 혁명을 주도하고 있습니다. '일자리의 미래'에 관한 세계 경제 포럼 보고서에 따르면 2018년에는 12개 산업에서 기계가 29%의 작업을 처리했지만, 2022년까지 모든 작업의 42%와 조직의 정보/데이터 처리 및 정보 검색 작업의 62%를 시스템과 알고리즘이 처리할 것이라고 합니다. 이는 불과 4년 만에 이루어지는 급진적 변화입니다. AI로의 여정은 잘 구축된 정보 아키텍처를 통해 이루어집니다. 대부분의 AI 실패는 AI 모델 자체가 아니라 데이터 준비 및 구성으로 인한 것입니다. AI 모델의 성공은 먼저 성공적인 데이터 수집 방법에 달려 있습니다. 강력한 데이터 기반을 수립해야만 데이터 위치에 관계없이 간편하고 액세스가 쉬워집니다. 이 강연에서 기술자인 아누프 쿠마르(Anup Kumar)는 기업이 성공적으로 AI를 도입하도록 안내하는 중요한 측면을 탐구합니다.

1. 점심 & 실질적 데이터 활용
{ name : ”Anup Kumar",
title : ”Chief Architect, Data & AI",
company : ”IBM",
location : ”Asia Pacific",
email : ”anup@sg.ibm.com"}

3. 81%
do not
understand the
data required for
AI
3
No amount of AI algorithmic
sophistication will overcome a
lack of data [architecture]
“
80%
of data is either
inaccessible,
untrusted or
unanalyzed
94%
74%
are committed to multicloud
use AI to modernize apps
of AI developers use open source87%

4. 4
The problem isn’t too much data for humans–it’s too little

5. COLLECT - Make data simple and accessible
ORGANIZE - Create a trusted analytics foundation
ANALYZE - Scale insights with AI everywhere
Data of every type, regardless of
where it lives
MODERNIZE
your data estate for an
AI and multicloud world
INFUSE – Operationalize AI with trust and transparency
The AI Ladder
A prescriptive approach to accelerating the journey to AI
5
AI

6. 6
ARCHITECTURE TECHNOLOGY DATAOPS
Successful AI Project

7. 7
ARCHITECTURE
Successful AI Project
ü Microservices-based architecture of smaller,
decoupled units that work together.
ü De-coupling of Compute and Storage for big
data management.
ü Flexible and agile data model.

8. 8
TECHNOLOGY
Successful AI Project
ü Container Platform - Managing and promoting images at
scale as they flow through your system.
ü Open Standard Open Ecosystem
ü Data Virtualization – Concept of a truly physical data
lake lacks agility.
ü Leverage GPU/FPGA to accelerate your DL model.

9. 9
Using OpenShift, Red Hat’s enterprise Kubernetes distribution, data
scientists can easily deploy any containerized ML stack at scale on
public or private infrastructure for training and querying models.

10. Distributed systems architecture​
•Horizontal scale & data locality​
•Resilience, 24x7
Document data model​
•Natural, flexible, and fast ​
•Accessed by powerful APIs
Fully featured​
•Surrounding tools
•Open Source, Developer focus
MongoDB Enterprise – Operational Data Collection
document-oriented storage system supporting a flexible data model that enables you to store data of any structure, and
provides a rich set of features, including full index support, sharding, and replication.

11. 11
AI Explainability
http://aix360.mybluemix.net/

12. 12
DATAOPS
App
Deployment
Data
New Requirements
& Engagement
DevelopmentTest
Monitoring
Retraining
Search for
Data
Acquiring Data/
Self Service
Model
building
ML Model
Deployment
Refining
Data
Continuous Delivery
of Applications
Continuous Delivery
of Insights
SQL
Spark
Data Warehouse
SQL/ NoSQL
Rest API

13. 13
Example of DataOps
Auto Data
Discovery
Profile: Data
Classify &
Term Assg
Data Curation
&
Stewardship Machine Learning &
Self-Services
Analytics
Auto Data
Quality
Extract,
Transform,
Cleansing,
Standardize
Metadata
Catalog
Define Data
Governance
Objectives
Measure &
Monitor
Enforce
(Policies &
Rules)
Intelligent Metadata Catalog (infused with ML)
Shopping for
Information
Dataops for on-boarding data
Data Sources
Systems of
Record
3rd Party Data
Social Media
News
Systems of
Engagement
Other External
Weather
Documents
KAFKA
Flexible
data model

14. 14
Pre-built Use Cases
Watson Applications
Hybrid Data Management
Db2 Family
Data Ops & Governance
InfoSphere Family
Watson
Machine
Learning
Watson
Knowledge
Catalog
Watson
Studio
Watson
OpenScale
Prepare Build ManageRun
The IBM Data and AI Portfolio
Everything you need for enterprise AI, on any cloud
Business and
technical services
Unified Hybrid Data and AI Platform
Cloud Pak for Data
Hyperconverged
System

15. IBM Cloud / April 2019 / © 2019 IBM Corporation

16. 실질적 데이터 활용
[Anup Kumar]
https://www.research.net/r/IBMSponsorSeoul