Machine Learning and AI with PowerPlatform

2. CPP07: Supercharge
Power Platform with AI
Dipankar Bhattacharya (KPMG) &
Andrew Ly (KPMG)

3. Speakers

4. Dipankar Bhattacharya
Associate Director
KPMG
Andrew Ly (MVP)
Associate Director
KPMG

5. Agenda

6. The AI Impact
The AI Landscape
Built-in AI with PowerPlatform
Microsoft Azure AI & ML
Custom AI for PowerPlatform
ARTIFICIAL INTELLIGENCE

7. The AI Impact

8. TOP SECTORS ADOPTING THIS TECHNOLOGY
THREE FACTORS ENABLING AI GROWTH
The overall artificial intelligence
market is expected to reach
US$16.06B by 2022
Natural language processing is
expected to hold the largest
market share by 2022
AI & Machine Learning: A Forecast

9. Reasons for adopting AI
AI will allow us to obtain or sustain
a competitive advantage
Why is your organisation interested in AI? *
AI will allow us to move into new
businesses
Incumbent competitors will use AI
New organisations using AI will
enter our market
Pressure to reduce costs will
require us to use AI
Pressure to reduce costs will
require us to use AI
Customers will ask for AI-driven
offerings
84%
75%
75%
69%
63%
61%
59%
* MIT Sloan Management School Study

10. AI in Customer Experience
Artificial Intelligent
0 0.5 1
Emotion
strength
Neutral
Boredom
Joy
Emotions found to have the single greatest impact on
customer decisions & customer experience
“If you don’t understand their emotions, you don’t understand your customers.”
-Forrester Research Group

11. The AI Landscape

12. Definition
Artificial Intelligence (AI) is the intelligence of machines and
the branch of computer science which aims to create it.
definition: intelligence
• Intelligence (noun)
- 1. the ability to acquire and apply knowledge and skills
- 2. a person with this ability
- 3. the gathering of information of military or political value
- ORIGIN ME: via Ofr. From L. intelligentia, from intelligere
‘understand’, var. of intellegere ‘understand’, from inter ‘between’ +
legere ‘choose’

13. AI Landscape
ARTIFICIAL
INTELLIGENCE
A program that can sense,
reason, act and adapt.
MACHINE
LEARNING
Algorithms whose
performance improve as they
as exposed to more data over
time.
DEEP LEARNING
Subset of machine learning in
which multilayered neural
networks learn from vast
amounts of data
1950’s 1960’s 1970’s 1980’s 1990’s 2000’s 2010’s

14. AI Architecture

15. Machine Learning
Machine Learning Algorithms
Supervised Learning
Regression
Predict continuous
valued output
[e.g. Predicting Stock Price
or House Price]
Classification
Predict Discrete valued
output (e.g. 0 or 1)
Two-
class
classific
ation
Multi-
class
classificat
ion
All data is labelled and the algorithms learn to predict the output from the
input data.
[classifying new data from known properties]
[e.g. historical stock prices can be used to hazard guesses at future prices.]
Unsupervised Learning
All data is unlabelled and the
algorithms learn to inherent
structure from the input data
[discovering hidden properties of data]
Clustering
Discover the inherent
groupings in the data, such
as grouping customers by
purchasing behaviour.
Anomaly
Detection
Identification of items
or events that do not
conform to an
expected pattern or to
other items present in
a dataset.
[fraud detection, for
example, any highly
unusual credit card
spending patterns]
Reinforcement Learning
Allows machines and software
agents to automatically
determine the ideal behaviour
within a specific context, in order
to maximize its performance.
[making the best decisions now to
maximize long-term reward ]
[common in robotics, where the set of
sensor readings at one point in time is
a data point, and the algorithm must
choose the robot's next action. ]
[It is also a natural fit for Internet of
Things applications.]
Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959)

16. Built-in AI with
PowerPlatform: AI Builder

17. /r/ProgrammerHumor

18. What is AI Builder?
• Enables creation of AI
models without code
• Easy training of AI models
• Easy use of AI models
within PowerApps & Flow
• Currently in Preview
• GA October 2019

19. AI Model Types
• Can a machine
predict what a
value will be?
• Can a machine
reliably read a
form?
• Can a machine
understand an
image?
• Can a machine
classify text
objects?
• How much
historical data
will I need?
• How much can a
form deviate?
• How many
images is used
to train this
model?
• What
classifications
can be applied?
Binary
Classification
Forms Processing Object Detection Text Classification

20. Get Started Today

21. Microsoft Azure AI and
ML

22. Microsoft AI Services
INFRASTRUCTURE
CPU, FPGA, GPU
Cosmos DB SQL DB SQL DW Data Lake Spark DSVM Batch AI ACS Edge
AI ON DATA AI COMPUTE
TOOLS
CODING & MANAGEMENT TOOLS
VS Tools
for AI
Azure ML
Studio
Azure ML
Workbench
DEEP LEARNING FRAMEWORKS
Cognitive
Toolkit
TensorFlow Caffe
Others (Pycharm, Jupyter Notebooks…)
Others (Scikit-learn, MXNet, Keras,
Chainer, Gluon…)
Prebuilt AI
(Azure Cognitive Services)
Conversational AI
(Azure Bot Service)
Custom AI
(Azure Machine Learning)

23. Microsoft AI

24. Azure ML Algorithms
Is this A or B?
Classification Algorithms
How much?
How Many?
Regression Algorithms
Is this weird?
Anomaly Detection
How is this
organised?
Clustering Algorithms
What should I
do now?
Reinforcement Learning
Algorithms
Which brings in more
customers: a $5 coupon or
a 25% discount?
What will my fourth quarter
sales be?
Which printer models fail the
same way?
If you have a car with pressure
gauges, you might want to
know: Is this pressure gauge
reading normal?
For a robot vacuum: Keep
vacuuming, or go back to
the charging station?

25. Azure ML Studio

26. Custom AI for
PowerPlatform

27. What algorithm to use?
The choice of a model affects (and is affected by)
• Whether the model meets the business goal
• How much pre-processing the model needs
• How accurate the model is
• How explainable the model is
• How fast the model is (in making predictions)
• How scalable the model is (building and predicting)

28. Deploying modelsIs your data ready?
Getting ready
Define Objective
Access and
Understand the data
Pre-processing
Historical Data
[features + labels]
Split
Training Data
[features +
labels]
Testing Data
[features +
labels]
Train Model
[model learns from training
data]
Score Model
[model predicts on testing data]
Evaluate Model
[compare predicted results and
true labels]
Future Data
[features only]
Score Model
[model predicts the future
data]
Prediction
Results
Choosing and Tuning
models

29. Data Import from CDS to Azure ML
Security Model
…
Azure Data Lake Storage Gen 2
COMMONDATA
MODEL
Standard Entities
Contact
Account
First Party Entities
Lead
Opportunity
Custom Entities
Donation
Membership
ISV Entities
Email Send
Web Form
Business Logic Plugins Sync Workflows Calculated and Rollup fields
COMMONDATASERVICE
Flows
POWER PLATFORM
Model Driven Apps Canvas Apps Admin & Monitoring
Power BI Tenant
Workspaces
Dashboards
Workbooks
Reports
Datasets
Data
Flow
CDS SDK
First Party Apps ISV Power Apps Power User Companion Apps
Azure Data
Services
CDSConnectors

30. Is your data ready?
Is your data relevant? Do you have connected
data?
Is your data accurate? Do you have enough data
to work with?

31. Deploying models – Model consumption
It’s important to know (as much as possible) how models are to be
consumed:
 A model that is consumed by a web app (like Dynamics 365) needs to be
fast
 A model that is used to predict in batch (e.g. building a Marketing
segmentation based on some prediction) needs to be scalable
 A model that updates a dashboard, as data streams in, may need to be
fast and scalable

32. Demo