Approachable AI

network: MSFTGUESTCode: msevent44uc

2

Introductions

Andrew KraemerSenior Lead Consultant – Data ScienceData and AI Practice

Andrew.Kraemer@CatapultSystems.com

Lee HarperPrincipal Data ScientistData and AI Practice

Lee.Harper@CatapultSystems.com

Agenda

9.00–9:30 Welcome, What is AI, what problems can it solve?

9:30–10.15 Lifting the covers on the Data Science process

10.15 Break

10:30–11:00 Staffing your AI capability

11:00–11:15 AI tools in Microsoft Azure

11:15 Break

11:25–11:45 Demonstration – Deploying an IoT Model using Azure

11:45–12:30 Frontiers of AI, Q and A

What is AI?

What Problems Can it Solve?

5

What Isn’t AI?

6

What Is AI?

Depends upon who you ask!

Artificial intelligence (AI) is the simulation of human cognitive

processes by machines, especially computer systems.

In practice, mathematical models are used to make decisions

based on available data, in the presence of uncertainty.

7

Is AI Just Hype?

• According to Gartner, 85% of AI

projects fall short of expectations

• Disconnect between expectations

and reality

• There are many problems where AI

has been proven to add substantial

value

8

Artificial Intelligence Breakthroughs

9

Why Now?

10

Importance of AI to an Organization

AI

value

86% of CEOs consider digital technologies and AI to be the priority of № 1 for their companies

SourcePwC General Directors Survey Companies

AI Value Drivers:

New types of income

Improved customer experience

Lower operating costs

Increased productivity

Increase asset efficiency

Risk reduction

11

Types of Advanced Analytics

Most AI in use today falls

into these categories

12

Computer Vision – Information From Images

Disease Diagnosis

Optical Character Recognition

Object Detection and Recognition

13

Natural Language Processing

Sentiment

Analysis – How

are citizens

feeling?

Automatic

Translation

Chatbots and

smart assistants

Speaker and

voice recognition

14

Prediction: What Will Happen

Regression / Forecasting:

Predict a future value

Classification:

Predict probability of outcome

School

Enrollment

Forecasting

House Price

Estimation

Identify students

at risk of failing to

graduate

Predictive

maintenance on

shale gas fields

The Machine Learning Workflow

16

Example Problem

A car dealership group has the following problem statement:

“We have a large potential customer

base. We would like to figure out

which of our customers are most

likely to buy a new car from us, so

that we can produce a highly

targeted marketing campaign. We

want to increase sales relative to our

current segmentation strategy.”

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Customer Profile

• A large automobile dealership group

• Almost 50 dealerships across multiple geographies

• Around 500,000 active customers

• Data on over 1.5 million customers

• Around 70 million customer interactions recorded

• Data exists on 5 different systems of record

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CDK Dealership Management

What Data Might We Get?

• There may not be common elements across all tables – making combining difficult

• Data may arrive completely unprocessed

Data Set

SalesforceThird Party

Financial DataOperations

ManagementInternet

EnquiriesPhone Calls

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Exploratory Data Analysis

• Identify biggest areas for improvement — is the problem worth solving?

• Investigate feasibility of the AI project

• Provide instant analytical insights

0 10 20 30 40 50 60

Dealership A

Dealership B

Dealership C

Conversion Rate at Time of Car Sale

20

Assembling Customer Journeys

• How do we assign data to a customer?

• Event based data:

• Also have non-event based attributes (e.g., demographic data)

• AI model is going to learn the history and demographic factors that tend to lead to a car being purchased

Service email

Called to book service

Vehicle Service

Special offer

Visited dealer

Took a test drive

?

21

Extracting Features From Data

• AI models read mathematics, not timelines!

• Feature engineering is the “art” of the AI field

• Utilize many customer journey descriptors — all can be expressed mathematically

- Number of cars previously purchased

- Length of customer tenure

- Price of any previously purchased cars

Can be used for

traditional

segmentation

Service email

Called to book service

Vehicle Service

Special offer

Visited dealer

Took a test drive

- Did a person come in

for oil changes

- Distance from home

address to dealership

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Setting up the AI Model

Training

set

Test

set

23

Training the AI Model

Only use the training set

Features LabelsAI Learns the

Patterns

Customer ID Number of cars

purchased

Customer tenure /

days

1 1 10

2 4 1024

3 2 5000

4 2 740

Customer ID Car purchased in last 5 years?

1 1

2 1

3 0

4 0

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Lead Scoring A Customer

• Use the test set – not seen by the model during training

• Best simulation of how it will perform in real life

Model

78 %

24 %

Since > 50%

Since < 50%

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How can we use the model?

Automation

• Incorporate this score into your marketing automation workflow

• Could target highest 25% of customers with one campaign

• Could then target next 25% of customers with different campaign

Augment human decision making

• Add the model score to customer profiles, so that staff can see it

• Empowers call center staff, aftersales staff, or pre-sales staff with more information about the customer

10 Minute Break

Staffing Your AI Capability

28

What Kind of Roles Exist?

Raw Data Deployed ModelModelling

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The Data Engineer

• Skilled at transforming raw data to usable data

• Skilled at automating data transformation and storage processes

• Can handle data at huge scale (terabytes +)

Azure

SQL DB

Azure

Data Lake

Azure Data

Factory

30

The Data Scientist

• Skilled at designing and building AI models to solve real problems

• Adept problem solvers and/or mathematicians

• May be a specialist (e.g., natural language processing expert) or generalist

Azure Machine Learning Service

Machine Learning Studio

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The Machine Learning Engineer

• Skilled at operationalizing machine learning models at scale

• Skilled at software engineering and cloud infrastructure

• Integrates machine learning into smart applications

Azure Machine Learning Service

Azure Kubernetes Service

Azure DevOps Azure Containers

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The Data Science Researcher

• Skilled at developing new mathematical techniques and technologies

• Publishes novel research, whitepapers or files for patents

• Likely has a PhD in a mathematical subject area

• Probably works in academia, Microsoft Facebook, etc. or an AI startup

Azure Virtual

Machines

33

Raw Data Deployed ModelModelling

Building an Internal AI Capability

Data Scientist

(Modelling)

Initial Team Members

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Build a Successful AI Capability

I know

enough to be

dangerous…

• Mentorship and continuing education

• Many analytical people can develop prototype

models based on low risk use cases

• Peer review from other technical staff and business

stakeholders

• Professional Data Scientists optimize those models,

and prepare them for deployment

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Kaggle – A Favorite of Citizen Data Scientists

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Kaggle – A Favorite of Citizen Data Scientists

The winning submissions

will then go to the

company’s data science

team for further

research and

productionization

AI Tools on Azure

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Why Do AI In the Cloud?

• Data security can be guaranteed

• Control over who can access data

• Compliance with data privacy legislation

• Data and AI governance is easier to manage

• Powerful computing resources are available

• Everyone can work with the same data sources

• Making models available through deployment is made easier

39

What AI Tools Are Available in Azure?

Zero Coding

Azure Machine Learning

Coding

Azure Machine Learning

Data Science Virtual Machine

Cognitive Services

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The Data Science Virtual Machine

Comes preloaded with:

And many more!

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Azure Machine Learning

• Data science platform, includes powerful AutoML functionality

• Manage the entire machine learning lifecycle

• Code and no-code solutions can collaborate in the same place

42

Azure Machine Learning

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Azure Databricks

• A powerful integrated data engineering and data science platform

• Allows for the use of distributed computing for big data processing

• Requires expertise in python or scala languages and spark syntax

1 TB hard drive

8GB of RAM

10 TB Dataset

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Cognitive Services

• Microsoft has built some models for some specific tasks

• People or intelligent apps can access these models through API calls

Content moderator – recognizes adult content or profanity

Speech services – convert spoken word to text or vice versa

Translator Text – automatic language detection and translation

QnA maker – create a chatbot that can answer questions

Computer vision – identify features in images, transcribe text from pictures of documents (OCR)

10 Minute Break

Demos

Frontiers of AI

48

Designing AI to Earn Trust

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Machine Learning Operations (MLOps)

Integrating key practices of software

engineering and DevOps with individuals

who contribute to the AI workflow

Improve productivity and insight quality through the automation of AI

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Prescriptive Analytics

?

• What actions can we take to achieve a desired outcome?

• AI will be able to tell us the series of actions to minimize the chance

of the student dropping out

• Achieves ultimate personalization

Student applies

Student accepted

Student attends course A

Student attends

course B

Student receives bill

for $1000

Student pays bill on time

?

51

Advanced Natural Language Processing– AI With Memory

• Recent advancements (late 2019)

• Essential for accurate translation and text comprehension

• Uses advanced neural network know as a “Transformer”

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Autonomous Robotics

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Sidney.Atkinson@catapultsystems.com

Lee.Harper@catapultsystems.com

Contact Details and Innovation Workshops

Q and A

55

Build and deploy models using

Azure Machine Learning

How do we use the model when it’s built?

• Azure provides powerful tools for using live models

Make predictions using secure

Azure webservices

JSON Request AI Model in Web

Service

JSON Response

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Example Problem

A higher education system has the following problem:

“We have a number of students that do not complete their 4-year degree programs. We would like to be able to predict which students are likely to drop out in a given year, so that we can provide additional support and resources.”

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What Data Might We Get?

Accounting

DataCourse CatalogCRM Data

Demographic

DataEnrollment

3rd Party

Data

Data Set

- There may not be common elements across all tables – making combination difficult

- Data may arrive completely unprocessed

58

Exploratory Data Analysis

• Identify biggest areas for improvement – is the problem worth solving?

• Investigate feasibility of the AI project

• Provide instant analytical insights

0 100 200 300 400 500 600

Campus A

Campus B

Campus C

Number of Students Dropping Out 2014-2019

59

Assembling Student Data

• How do we assign data to a student?

• Event based data:

• Also have non-event based attributes – eg demographic data

• AI model is going to learn which event history and demographic factors tend to lead to the outcome of a person dropping out of college

Student

Applies

Student

Accepted

Student

attends

course A

Student

attends

course B

Student

receives bill

for $1000

Student

pays bill

on time

?

60

Extracting Features From Data

• AI models read mathematics, not timelines!

• Feature engineering is the “art” of the AI field

Student

Applies

Student

Accepted

Student

attends

course A

Student

attends

course B

Student

receives bill

for $1000

Student

pays bill

on time

• - Utilize many student journey descriptors – all can be expressed mathematically

- Cumulative GPA

- Semester hours

- Number of completed semesters

- Number of late payments

- Distance of home from campus

61

Setting up the AI Model

Training set

Test set

62

Training the AI Model

• Only use the training set

Student ID Cumulative GPA Average hours per semester

1 3.20 12.6

2 3.96 15.8

3 2.46 13.2

4 3.42 13.4

Student ID Dropped out?

1 1

2 0

3 0

4 1

Features LabelsAI Learns the

Patterns

63

Scoring A Student

• Use the test set – not seen by the model during training

• Best simulation of how it will perform in real life

Model

78 %

24 %

Since > 50%

Since < 50%

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How can we use the model?

Automation

• Send automatic check-in email campaign targeting students at risk of dropping out

• Could target highest 10% at risk

• Or could target all students with a score greater than, say, 75%

Augment human decision making

• Add the model score to student profiles, so that staff can see it

• Empower them to be aware of students that may be experiencing difficulties