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The cloud in examples:
(how to) benefit from
modern technologies
in the cloud
Petr Filas
September 2021
2
This webinar begins shortly
› About the speaker
– Senior Data Consultant & Head of Profinit Azure Competency
– 13 years of professional experience in Data Integration, Data Management, Data Warehousing
– Data Architect, Solution Architect, Management Consultant
– O2, Prague Airport, Česká Spořitelna
23 years
on market
(since 1998)
757 mil.
(CZK) turnover
in 2020
Finance
& Telco
Significant
customers
550+
experienced
professionals
#2
CAD in CZ
(IDC 2017)
CZ
+ 10 EU
countries
› About Profinit
3
Our competence
APPLICATION
OUTSOURCING
ENTERPRISE
INTEGRATION
BUSINESS
INTELLIGENCE/DWH
SOFTWARE
DEVELOPMENT
BIG DATA
AND DATA SCIENCE
4
About this webinar
› Cloud introduction
/*Very brief*/
› Practical demonstration of
two different cloud use cases
› Because it’s {practical},
we will focus on solutions
in Microsoft Azure
› Two scenarios
– Building a public API integration
application—connecting
{legacy} systems to a public
API provider to deliver
a modern user experience
– Building an ML /*Machine
Learning*/ application—
leveraging cognitive services
for business process digitization
5
Cloud introduction
› The cloud is everywhere
around us…
› “Modus operandi” aka how
each of us uses the cloud
› Personal perspective
– We consume/produce content
• We consume resources
› Professional perspective
– We create solutions based
on cloud services
• We mediate resource consumption
6
Cloud introduction
› Reasons for using the cloud /*Choose your favorite one*/
{Accessibility}
from literally anywhere
Ready to use out of the box
“we don’t need an IT department”
{Elasticity}
cost-efficient
Usually has better
{security}
I’m a millennial and the cloud
is just a cool thing to use
7
Cloud services
› It’s impossible to know all
cloud services, even just
for one single cloud provider
(Azure has more than
200 services)
› Services are changing
very rapidly
› The question is: How do
I choose the right service?
– Either {experience}
or {learn how}
› We will focus on the following
services in Azure
– {AppService}—very popular and
well-known service for application
runtime in the cloud—“PaaS
application server”
– {Computer Vision} + {Machine
Learning}—maybe not so well-
known services that provide image
processing
• Out-of-the-box or with low-code
for simple scenarios
• Fully equipped ecosystem
for complex scenarios
8
Scenarios introduction
› Imagine you are responsible for running an {airport},
and you want to use the cloud to improve your business
› What is the biggest problem for airports?
Delays
9
Scenarios introduction
› There are delays that are out of your control
(e.g., technical difficulties, safety, capacity)
› Therefore, we will focus on delays you can mitigate,
and we have just picked two of them
– {Scenario 1}
• Your airport wants to improve
passengers’ information
awareness to reduce delays
caused by passengers
boarding late
 give passengers easy
access to flight information
– {Scenario 2}
• You want to evaluate the flight
handling process (on/off-loading
baggage, fueling, cleaning, etc.)
in real time for early detection of
possible delays
 start object and task
detection from the CCTV stream
Building a public API integration
application—“Connecting {legacy}
systems to a public API to deliver
a modern user experience”
1
11
Story A requirements
› The goal
– Improve passengers’
information awareness to
reduce delays caused by
passengers boarding late
› The approach
– Let passengers use their
favorite messaging app to
receive flight updates
– Create a connection between
your legacy systems and social
media/apps
› The example for this scenario
was created for Facebook
Messenger
› Easy to use: Scan the QR
and subscribe by entering
the flight number
12
Design and architecture
Legacy
systems
AppService
application
Azure
Data
update feed
Data
update feed
Subscription
requests
13
Showcase
14
Demo
15
16
Conclusion and benefits
Applicable to other
messaging platforms
A simple subscribe and
publish scenario that
enables you to connect
your legacy system to
modern communication
channels
No need for a DMZ,
the “bridge” app runs
(safely) in the cloud
Use existing wide-spread
messaging platforms
instead of traditional
methods (e.g., email)
or your own app
Building an ML /*Machine Learning*/
application—“Leveraging cognitive
services out-of-the-box for business
process digitization”
2
18
Story B requirements
› The goal
– Evaluate the flight handling
process (on/off-loading
baggage, fueling, cleaning, etc.)
in real time for early detection
of possible delays
› We will leverage the potential
of ML in Azure
› The approach
– Detect and check events on the
ramp during the aircraft
handling process
– At airports, surveillance
cameras are there for such
purposes, but staff only use
them if there is a problem
– This means that the continuous
service quality of handling
procedures usually isn’t
evaluated in real time
Detect the cause of delays
20
How does it work /*overall*/?
Our task is to recognize
objects from a picture
(a CCTV stream is just
a stream of pictures)
The {recognition
algorithm} must be pre-
trained on labeled input
= {supervised learning}
Then the algorithm
is used to detect
and label the objects
in new unlabeled
(real-life) input
Labeled training data Learning process ML model
Real-life data ML model
1
2
Labeled
real-life data
21
How does it work with out-of-the-box models?
Simple example: Create
a {Computer Vision}
resource in Azure and
start {Object Detection}
using API calls
⚠️ This resource
uses a general model
that is trained to
recognize common
objects
This model is not trained
to recognize specific
objects  a lower ratio
of successful recognition
is to be expected
22
Simple scenario with an out-of-the-box model
Image credits:
https://commons.wikimedia.org/wiki/File:Vienna_International_
Airport_from_the_Air_Traffic_Control_Tower_06.jpg
Stanislav Doronenko
https://commons.wikimedia.org/wiki/File:Qatar_Airways_Airbu
s_A380800_at_Heathrow_Airport_Terminal_4_before_Flying_
to_Doha,_6_Jan_2015.jpg
Mohammed Tawsif Salam
23
24
Simple scenario: Result #1
25
Simple scenario: Result #2
26
How does it work when things get serious?
› In more complex scenarios, API calls are less effective
› We need to process stream/pictures directly using the image processing engine
in {Python}
› For this purpose, we create the resource {Machine Learning} that provides {Machine
Learning Studio} features like preprocessing, Python scripting, model tuning, etc.
27
How does it work when things get serious?
Compute cluster
Python scripts
Pre-trained
model
Raw input video stream Augmented output stream
+
+
28
Demo with a custom model
Video stream credits:
https://www.webcamtaxi.com/en/japan/nagasaki/nagasaki-airport.html
29
30
Conclusion
Follow up: take the extracted
data and build logic on it
(early warnings)
With easy tasks, you
benefit from an easy setup
and get results fast, but
please note that these
results might not be so
accurate
With more challenging
tasks, some coding is still
needed (preprocessing,
combining video stream
and data, etc.)
31
Overall conclusion
› In the cloud, you can benefit from
many low- or no-code services that
are ready to use out-of-the box
› The tricky part is how to choose
the right one - experience
or learn how
› The cloud is a fast-track option for
delivering solutions: With easy
scenarios, the path is very quick
and effective, but if the solution is
more complex, you still need a bit
of engineering (no silver bullet)
› With this approach a new
challenge arises: The less you
have to focus on deployment and
implementation tasks, the more
you have to pay attention to
security, architecture, and how to
use these services the right way
32
Discussion
Profinit EU, s.r.o., Tychonova 2, 160 00 Prague 6
Tel.: + 420 224 316 016, web: www.profinit.eu
LinkedIn
linkedin.com/company/profinit
Twitter
twitter.com/Profinit_EU
Facebook
facebook.com/Profinit.EU
Youtube
Profinit EU
We need your help to be better!
Since you are here, please help us
improve our events and vebinars and
take a look at our short survey. We
appreciate your interest to help us grow.
The webinar has ended.
Thank you very much for attending!
Petr Filas
Consultant
petr.filas@profinit.eu

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Cloud in examples—(how to) benefit from modern technologies in the cloud

  • 1. The cloud in examples: (how to) benefit from modern technologies in the cloud Petr Filas September 2021
  • 2. 2 This webinar begins shortly › About the speaker – Senior Data Consultant & Head of Profinit Azure Competency – 13 years of professional experience in Data Integration, Data Management, Data Warehousing – Data Architect, Solution Architect, Management Consultant – O2, Prague Airport, Česká Spořitelna 23 years on market (since 1998) 757 mil. (CZK) turnover in 2020 Finance & Telco Significant customers 550+ experienced professionals #2 CAD in CZ (IDC 2017) CZ + 10 EU countries › About Profinit
  • 4. 4 About this webinar › Cloud introduction /*Very brief*/ › Practical demonstration of two different cloud use cases › Because it’s {practical}, we will focus on solutions in Microsoft Azure › Two scenarios – Building a public API integration application—connecting {legacy} systems to a public API provider to deliver a modern user experience – Building an ML /*Machine Learning*/ application— leveraging cognitive services for business process digitization
  • 5. 5 Cloud introduction › The cloud is everywhere around us… › “Modus operandi” aka how each of us uses the cloud › Personal perspective – We consume/produce content • We consume resources › Professional perspective – We create solutions based on cloud services • We mediate resource consumption
  • 6. 6 Cloud introduction › Reasons for using the cloud /*Choose your favorite one*/ {Accessibility} from literally anywhere Ready to use out of the box “we don’t need an IT department” {Elasticity} cost-efficient Usually has better {security} I’m a millennial and the cloud is just a cool thing to use
  • 7. 7 Cloud services › It’s impossible to know all cloud services, even just for one single cloud provider (Azure has more than 200 services) › Services are changing very rapidly › The question is: How do I choose the right service? – Either {experience} or {learn how} › We will focus on the following services in Azure – {AppService}—very popular and well-known service for application runtime in the cloud—“PaaS application server” – {Computer Vision} + {Machine Learning}—maybe not so well- known services that provide image processing • Out-of-the-box or with low-code for simple scenarios • Fully equipped ecosystem for complex scenarios
  • 8. 8 Scenarios introduction › Imagine you are responsible for running an {airport}, and you want to use the cloud to improve your business › What is the biggest problem for airports? Delays
  • 9. 9 Scenarios introduction › There are delays that are out of your control (e.g., technical difficulties, safety, capacity) › Therefore, we will focus on delays you can mitigate, and we have just picked two of them – {Scenario 1} • Your airport wants to improve passengers’ information awareness to reduce delays caused by passengers boarding late  give passengers easy access to flight information – {Scenario 2} • You want to evaluate the flight handling process (on/off-loading baggage, fueling, cleaning, etc.) in real time for early detection of possible delays  start object and task detection from the CCTV stream
  • 10. Building a public API integration application—“Connecting {legacy} systems to a public API to deliver a modern user experience” 1
  • 11. 11 Story A requirements › The goal – Improve passengers’ information awareness to reduce delays caused by passengers boarding late › The approach – Let passengers use their favorite messaging app to receive flight updates – Create a connection between your legacy systems and social media/apps › The example for this scenario was created for Facebook Messenger › Easy to use: Scan the QR and subscribe by entering the flight number
  • 15. 15
  • 16. 16 Conclusion and benefits Applicable to other messaging platforms A simple subscribe and publish scenario that enables you to connect your legacy system to modern communication channels No need for a DMZ, the “bridge” app runs (safely) in the cloud Use existing wide-spread messaging platforms instead of traditional methods (e.g., email) or your own app
  • 17. Building an ML /*Machine Learning*/ application—“Leveraging cognitive services out-of-the-box for business process digitization” 2
  • 18. 18 Story B requirements › The goal – Evaluate the flight handling process (on/off-loading baggage, fueling, cleaning, etc.) in real time for early detection of possible delays › We will leverage the potential of ML in Azure › The approach – Detect and check events on the ramp during the aircraft handling process – At airports, surveillance cameras are there for such purposes, but staff only use them if there is a problem – This means that the continuous service quality of handling procedures usually isn’t evaluated in real time
  • 19. Detect the cause of delays
  • 20. 20 How does it work /*overall*/? Our task is to recognize objects from a picture (a CCTV stream is just a stream of pictures) The {recognition algorithm} must be pre- trained on labeled input = {supervised learning} Then the algorithm is used to detect and label the objects in new unlabeled (real-life) input Labeled training data Learning process ML model Real-life data ML model 1 2 Labeled real-life data
  • 21. 21 How does it work with out-of-the-box models? Simple example: Create a {Computer Vision} resource in Azure and start {Object Detection} using API calls ⚠️ This resource uses a general model that is trained to recognize common objects This model is not trained to recognize specific objects  a lower ratio of successful recognition is to be expected
  • 22. 22 Simple scenario with an out-of-the-box model Image credits: https://commons.wikimedia.org/wiki/File:Vienna_International_ Airport_from_the_Air_Traffic_Control_Tower_06.jpg Stanislav Doronenko https://commons.wikimedia.org/wiki/File:Qatar_Airways_Airbu s_A380800_at_Heathrow_Airport_Terminal_4_before_Flying_ to_Doha,_6_Jan_2015.jpg Mohammed Tawsif Salam
  • 23. 23
  • 26. 26 How does it work when things get serious? › In more complex scenarios, API calls are less effective › We need to process stream/pictures directly using the image processing engine in {Python} › For this purpose, we create the resource {Machine Learning} that provides {Machine Learning Studio} features like preprocessing, Python scripting, model tuning, etc.
  • 27. 27 How does it work when things get serious? Compute cluster Python scripts Pre-trained model Raw input video stream Augmented output stream + +
  • 28. 28 Demo with a custom model Video stream credits: https://www.webcamtaxi.com/en/japan/nagasaki/nagasaki-airport.html
  • 29. 29
  • 30. 30 Conclusion Follow up: take the extracted data and build logic on it (early warnings) With easy tasks, you benefit from an easy setup and get results fast, but please note that these results might not be so accurate With more challenging tasks, some coding is still needed (preprocessing, combining video stream and data, etc.)
  • 31. 31 Overall conclusion › In the cloud, you can benefit from many low- or no-code services that are ready to use out-of-the box › The tricky part is how to choose the right one - experience or learn how › The cloud is a fast-track option for delivering solutions: With easy scenarios, the path is very quick and effective, but if the solution is more complex, you still need a bit of engineering (no silver bullet) › With this approach a new challenge arises: The less you have to focus on deployment and implementation tasks, the more you have to pay attention to security, architecture, and how to use these services the right way
  • 33. Profinit EU, s.r.o., Tychonova 2, 160 00 Prague 6 Tel.: + 420 224 316 016, web: www.profinit.eu LinkedIn linkedin.com/company/profinit Twitter twitter.com/Profinit_EU Facebook facebook.com/Profinit.EU Youtube Profinit EU We need your help to be better! Since you are here, please help us improve our events and vebinars and take a look at our short survey. We appreciate your interest to help us grow. The webinar has ended. Thank you very much for attending! Petr Filas Consultant petr.filas@profinit.eu