building ai solutions @ scale · iot and smart machines artificial intelligence quantum computing...

BUILDING AI SOLUTIONS @ SCALE

Dr. Arati DeoManaging DirectorAI Practice Lead, India

Copyright © 2018 Accenture All rights reserved. 2

WE ARE IN AN UNPRECEDENTED PERIOD OF TECHNOLOGY INNOVATION

Mainframe Client-Server and PCs

Web 1.0 eCommerce

Web 2.0, Cloud, Mobile

Big Data, Analytics, Visualization

IoT and Smart Machines

Artificial Intelligence

Quantum Computing

Quantum Computing

1950 Turing Test

Artificial Intelligence

Mainframe

Client Server and PCs

Web 2.0, Cloud, Mobile

IoT and Smart Machines

Big Data, Analytics, Visualization

Web 1.0 Ecommerce


WHAT IS ARTIFICIAL INTELLIGENCE (AI)?More natural interactions between people and machines

Multiple Technologies that enable computers to

• Sense (e.g., computer vision, audio processing or sensor processing)

• Comprehend (e.g., natural language processingor knowledge representation)

• Act (e.g., inference engines, predictions or expert systems)

• Learn and self-tune (e.g., machine learning, deep learning)

Expert Systems

Computer Vision

Inference Engines

Machine Learning

Robotic Process

Automation

Deep Learning

Sensor Processing

Knowledge Repre-

sentationMini Bots

EmotionRecognition

Gesture Recognition

Ontologies

Neural Networks

BiometricsNatural

Language Processing

Video Analytics


WHAT’S DIFFERENT ABOUT AI SOLUTIONS

AI Development

Lifecyle

Business Requirement to AI

Problem Defn.

Model Development

Production Data Pipeline

Ongoing monitoring & evaluation

Data Collection

Model Deployment

Heavy Data Dependence

Probabilistic Solutions

More complex to debug and

support

New, less-understood technology

Choice of solution is non-unique

5

AI@SCALE SOLUTION – KEY CHARACTERISTICS

Proven ROIVolume

Reliable

Repeatable Evolutionary

Operating on volumesbeyond pilot/prototype

ROI proven on production volumes

Engineered for reliableoperation and quality

Repeatable methodology for other

similar solutions

Enables evolution to increasing automation


BUILDING AI@SCALE – 1/5 – BUSINESS-VALUE FOCUSED APPROACH

#1

BusinessValue-focused

• Strong Industry and Technology partnership to experiment and evolve the solution– Conversion of business problem to the right AI problem

– Right use of the AI outputs in downstream processes

– Ongoing ROI measurement

• Maintain a problem-first approach, find the simplest solution– Leverage human-computer interactions to simplify


PROCESS AUTOMATION - QUOTE AMENDMENT (HIGH LEVEL FLOW)

Systems

Sales Rep

Advisor

Advisor asks if the seller wants to create an alternative quotes with increased Service Levele.g. - “Would you like to create alternative quotes with increased service level?”

Create Alternative quotes

Navigates to ‘Deal Info’ tab on Opportunity with Quote

AI Engine

SFDC

SAP

For making changes to an individual quote advisor redirects seller to [SSQ Link]

No

Advisor creates alternate quotes for all the quotes under consideration

Create alternate quotes for all these

and change

SL?

Advisor identifies the opportunity related to the account and also recognizes the quotes which have identical SLs and may need same SL upgrade

Advisor recommends the next (logical) increased level of service based on current SL

Advisor adds the new Service Level to all the quotes and confirms the creation of alternate quotes

Advisor moves to the next change,

if applicable

Advisor recommends the Service Level to be added or lists all the Service Levels to choose from

Seller feeds in the Service Level

Quotes are on SFDC opportunity, viewable to the seller through the UI. Changes are made in the Service Level field.

Yes

Seller confirms


INDUSTRY AND TECHNOLOGY PARTNERSHIP THROUGHOUT DEVELOPMENT LIFECYCLE

AI Development

Lifecyle

Business Requirement to AI

Problem Defn.

Model Development

Production Data Pipeline

Ongoing monitoring & evaluation

Data Collection

Model Deployment


BUILDING AI@SCALE – 2/5 – DIVERSE ECOSYSTEM FOR AI DEVELOPMENT

#2

Diverse Ecosystem

• Diverse skills employed for development

• Partnerships with academia to deliver thought leadership and innovative solutions– Community approach to team development

• Relationships with key technology partners and start-ups


DIVERSE & EVOLVING ROLES FOR AI DEVELOPMENT

SHAREDDEDICATED

DEDICATED & SHARED

Designs and implements the new user interfaces and visualization mediums

needed for AI systems

Visualization Designer/Engineer

Sustain AI assets and platforms; recruit AI talent; create training

programs for upskilling for AI skills

AI DeliveryLeads

Data Engineer

Designs and builds the data pipelines for data processing and

ingestion to the AI system

Business Analyst

Contributes in converting business problem to relevant AI

problem; user acceptance testing

Builds overall framework for AI deployment, designs the architecture

for AI production setup

Ai ArchitectAI/ML Scientist

Develops model design; conducts various experiments to determine best

model: creates final best model

AI/ML Engineer

Implements code and services to process data and generate desired outputs from final model in

production system


AI Ecosystem and partnerships

AI technology radars Global alliances

Accenture Investment

$600 million - including 185+ Related Patents and Applications

Total # of practitioners

2200+

University / Research partnerships

• Stanford• MIT

• DFKI • Turing

institute

Global Labs

• UK Liquid Studio

• Dublin AI COE• Tech Labs Palo

Alto

• Tech Labs Sophia

• IDC Innovation Centre


BUILDING AI@SCALE – 3/5 – USE TECH-AGNOSTIC FRAMEWORKS

• Develop integrated solutions that leverage best-of-breed products

• Create processes to continuously evaluate new technology and software solutions

#3

Technology-agnostic


Technology-agnostic Architecture Framework for AI

Custom Services

Sources

Doc Types

Channels

Te

ch

nic

al S

erv

ice

s

AI T

ra

inin

g

System

Enterprise

Systems

Business Services

Data Services

AI Interface Services

AI Services

AI Platforms/Libraries

ACIP

Fraud Detection

Service

Feedback Service

Answer Service

Orchestration

Services

Natural Language

Processing

Speech

Recognition

Image

Processing

Data

Insights……

AI Platforms (Azure, IBM Watson, …) AI Libraries (Python, R …)

Data

Transformation

Data

MaskingOCR

Persistence

ServicesData Lake

Social Media

Enterprise

Systems

Enterprise

Free Text

XML

XML

</>

Email/Fax/Voice Mail

Health Advisor

Re-admission Service

Cross Industry

Services

Smart Metering &

Outage Resolution

Predictive Network

Operations Service

Product Sale

Prediction Service

Visualization Services


Solution

Business Context

CASE STUDY – COMBINATION OF MULTIPLE TECHNOLOGIES

• The Client (an APAC Broadcasting Network) has requirement to identify new telecom poles as part of an acquisition process• The project team has 18 resources are working to identify the poles manually. This incurs heavy cost and is also time consuming.• Each pole is located in Google Maps manually and the type of pole (Utility Pole or Telecom Pole) is visually identified• Sample Images

Benefits

• Automatic identification of different types of poles (despite differences in backgrounds, angles, resolutions, distances)

• Over 35% automation of image classification problem for pole types (and significantly lower manual intervention)

• The images are downloaded using Google Street API• Automated image identification using Pattern Matching with

Google’s Tensor Flow Machine Learning Framework

• The Inception Model of Tensor Flow is based on Convolutional Neural Networks

• Final layers of inception model are re-trained with pole images to classify poles based on patterns

Telecom Pole Utility Pole


BUILDING AI@SCALE – 4/5 – THINK INDUSTRIAL SOLUTION (NOT PROTOTYPE!)

• Data and technology platforms built to scale– Flexible and Robust Data pipeline is crucial

• Industrialized services and cloud capabilities optimized for AI delivery

• Repeatable and sustainable development processes

#4

IndustrializedDevelopment

REPEATABLE DEVELOPMENT METHODOLOGY

Program Management

Project Management

Change Enablement

Service Introduction

De

plo

y R

ele

as

e

Se

rv

ice

De

liv

ery

AI Value

Targeting

Initiation

AI

Pil

ot

Pil

ot

De

plo

ym

en

t

Pil

ot

Op

era

tio

n

Solutioning

Co

nfi

rm

Co

nfi

rm

Project Execution

Sprint

Management

Release 1..n


Fraud Detection Solution at Scale

Courtesy: FICO Fraud Manager


BUILDING AI@SCALE – 5/5 – STRATEGIC CHANGE MANAGEMENT INCLUDED

• Evolve the level of automation through incremental steps

• Design approach that puts humans at the center of the solution– Incorporate change management plans in the development plan

• Involve the end users in design and measurement

#5

Strategic Change Management


AUTOMATION JOURNEY

Give Me the Facts

Give Me a Suggestion

Do This Task for Me

Take Responsibility Until

I Tell You Otherwise

Take Responsibility andDon't Let Me or Anyone Else Mess It Up

General Information

Specific Information

Advisory Guidance

Opt-In Automation

Overrideable Automation

Nonoptional Automation

Help Me as I Go

Courtesy: Gartner


AUTOMATION THRESHOLD TO OPTIMIZE BUSINESS VALUE

Source: Internet

Automation Threshold


BUILDING AI@SCALE – RECAP

#1 #2 #3 #4 #5

BusinessValue-focused

Diverse Ecosystem

Technology-agnostic

IndustrializedDevelopment

Strategic Change Management

Volume Proven ROI Reliable Repeat-

ableEvolution

-ary AI @ SCALE


QUESTIONS

Contact me at –[email protected]

Thank You

building ai solutions @ scale · iot and smart machines artificial intelligence quantum computing...

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