Download pdf - Artificial Intelligence - the next generation of automation

Bimal Tripathi Oct 2017

Bimal Tripathi

Sr Director

Brocade Communications

Trends and Opportunities

In Information Technology


Enterprises Artificial Intelligence

TOPICS• Enterprise adoption

• Demystifying AI

• Business Case for Enterprise AI Strategy

• Enterprise AI Roadmap

• Ethics


Enterprise adoption…

Enterprises do not deal with smart

cars, natural language processing,

Computer vision and photo tagging

but…

… would benefit from AI by using

cumulative enterprise digital

experience to

• Grow revenue

• Improve products

• Enhance Customer Engagement

• Optimize operating efficiencies


2

Demystifying AI - How it works?Computer learns from data

1

3

Recognizes patterns in the historic data

higher dimensions and larger data volumes

Builds models (statistical rules ) to predict

Applied the rules on new data to make

decisions


Historic Data

Pattern

Rule48.5 + 0.05 * Revenue

Example 1: Supervised Learning – Regression

What is the right deal size for a customer given Revenue $6,040 M?


Will App crash?CPU Utilization : 70%Memory Utilization : 85%

Historic Data

Pattern

Rule – A decision tree

Example 2: Supervised Learning – Decision Tree

Predict Service disruption - is the service availability at risk?


Historic Data

Rule – a neural network

Example 3: Supervised Deep Learning (Neural Networks)

Lead quality prediction – what leads should internal sales reps work on?


Personalized offers

and messaging

Example 4: Unsupervised Learning

Customer Segmentation – group “look alike customer” for the next campaign

CUSTOMER Data CLUSTERED DATA


Web Traffic

Response time

Example 5: Unsupervised Learning

Anomaly detection – Real or False Alarm?


Business case for Enterprise AI Strategy

Last 3 decades of business innovation…

ERP -> Internet -> Analytics -> Virtualization -> Cloud -> Mobile

Is AI the next frontier?

Strategic GoalsGrow revenue

Enhance Customer Engagement

Improve products

Optimize operating efficiencies

ExecutionSuccess Metrics

Process Owners

Governance

InfrastructureData Science Lab

Data Architecture

At scale Computing

HDFS/SPARK/PYTHON


Sales/CRM Product

Operations

Marketing &

eCommerce

Information

Technology

People

Operations

Enterprise Artificial IntelligenceUse case for Enterprise functions

Bot and assistants Opportunity ranking

PricingCustomer churnSupport readinessSentiment

Product FailureInventory MgmtForecastingAnomaly

detection

Lead scoringRecommendationsPromotions Segmentation

Dynamic pricingBuyer intent

Cyber SecurityHelp Desk automationData Quality

System availability

Recruiting EngagementAttrition

Data sources:

• In-house applications – ERP, Web, CRM, products

• Data Warehouse/Data Lake/Big Data Clusters

• 3rd party data providers

Bimal Tripathi Oct 2017© 2016 BROCADE COMMUNICATIONS SYSTEMS, INC. Brocade Confidential.

12

PERFORMANCE DATACOLLECTED• CALL HOME DATA• LOGS• SW AGENTS

PRODUCT DISRUPTION EVENTS• RETURNS• SERVICE

REQUESTS

AGGREGATE, CLEAN ENRICH

TO FORM SUPERVISED

LEARNING TRAINING DATA

BUILD/TRAIN MACHINE LEARNING MODEL

PRODUCTS IN FIELDSERVICES IN CLOUD

ALERT CUSTOMERTAKE CORRECTIVE ACTIONBEFORE CUSTOMER CALLS

Solve your customers’ problems before they call youPredict product failures and Service disruptions using Supervised

Learning

COLLECT NEWPERFORMANCE DATA

1 2 3

4 5

PREDICTPREDUCT DISRUPTIONNEW DATA

6

7

8

4


PREPARED FOR AN ENTERPRISE AI STRATEGY?

1- Align and

Plan2- POC &

Simulation

3- Design &

Build

4- Track &

Monitor

Establish ML goal

Build domain knowledge

and engage SME

Build hypotheses and

experiment

Determine the sources

of data

Define success criteria and

performance metrics

Find strongest

predictors of event

Select model and run

train/test validation

Collect and enrich data

for model building

Iterate and pick best

model

Design deployment

architecture for scale

Select Machine

Learning Technologies

Identify data sources and

needed process changes

Deploy new data taps

Institute systemic data

Quality validations

Deploy for production

usage

Monitor Prediction accuracy

& adjust Model parameters

Study Performance

Trends

Root cause and correct

performance detractors

Assess cycle time to

results throughput SLAs

Conduct capacity review

and technology upgrades

Sample tools and technology:DashboardsVisualization ToolsMonitoring tools

Sample tools and technology:Database, ExcelR, Python, SAS, RapidMiner, TensorFlow, H2O, Spark Mlib,Amazon AML

Sample tools and technology:Dashboards Visualization tools

Roadmap to implementing Enterprise Machine Learning

Sample tools and technology:Data repository (RDBMS, HDFS)Python, Java, Spark, RInfrastructure: AWS, Google, SFDC, Oracle, SAP, IBM, MSFT


Ethics of Artificial Intelligence

❖Bots versus humans

❖Social Economics

❖Employee privacy

❖Customer profiling
