Global Cloud Hyperscale Vendors - The top three (3) for MLaaS (AWS, Azure, GCP) - What is NoOps, Cognitive AI, and MLaaS - What is Differentiating Cognitive AI MLaaS NoOps Vendors Using Cognitive AI MLaaS NoOps services - Vision Image - Vision Video - Audio - Language & Text Approach for rapid Business Impact

1. Cloud Hyperscale Vendors
Cognitive Artificial Intelligence
NoOps MLaaS Services
Action Projects with Björn Rodén

2. Session Objectives
• This session focus on using Cloud Vendors Cogni.ve Ar.ficial Intelligence MLaaS NoOps
• We will explore the top three (3) major Global Cloud Vendors* offerings and usage:
• Amazon Web Services (AWS)
• Microso4 Azure Cloud (Azure)
• Google Cloud Pla<orm (GCP)
• We will discuss a prac;cal approach for star;ng AI projects
• We will focus on core Cogni;ve Ar;ficial Intelligence NoOps services:
• Vision Image
• Vision Video
• Audio
• Language
• We will touch on OpenCV
• We plan to do demos (;me & technology permiFng)
• We will not cover:
• Machine Learning algos
• Deep Learning models
• Data Science & Engineering concepts
• Edge Inference or TinyML
• MLperf
You will learn
considera/ons for
and how to use
Cloud Vendors
Cogni/ve AI NoOps
- programma/cally -
objec&ve
© Björn Rodén 2
December 2019 @ Dubai Data Science Fest
Nota Bene: This presenta0on is adapted from a 2-day workshop

3. Agenda
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⚛Introduc)on
⚛ Global Cloud Vendors – the top three (3) for MLaaS
⚛ What is NoOps, Cogni/ve AI, and MLaaS
⚛ What is Differen/a/ng Cogni&ve AI MLaaS NoOps Vendors
⚛Approach for rapid Business Impact
⚛Using Cogni&ve AI MLaaS NoOps services
⚛ Vision Image
⚛ Vision Video
⚛ Audio
⚛ Language
⚛Next steps

4. Global Cloud Vendors – the top three (3)* for MLaaS
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1. Amazon Web Services (AWS)
• h"ps://aws.amazon.com/machine-learning/
2. MicrosoC Azure Cloud (Azure)
• h"ps://azure.microso6.com/services/cogni8ve-
services/
3. Google Cloud PlaIorm (GCP)
• h"ps://cloud.google.com/products/ai/
* h#ps://www.datama.on.com/cloud-compu.ng/aws-vs-azure-vs-google-cloud-comparison.html
1
2
3

5. What is NoOps
• No Opera&ons Services (NoOps) for Customers to the Cloud Vendors
• Vendor provided & managed services
• API services but not [exposed] micro-services
• Cost model – pay as you go (use)
• A8er a free cap is reached (vendor dependent)
• No need for own infrastructure
• Either on-prem, hosted or cloud provided
• No need for staff to maintain soDware or hardware
• However, Data Governance is imperaDve, as are Ethics, Security and Privacy Strategies
• No need for extra Data Scien&sts or Engineering
• Curious staff with apDtude and moDvaDon for self- learning [logic thinking]
• Self-Learning provided free by Cloud Vendors* over Internet, through Googling and YouTube
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* Especially from the top-three Cloud Vendors

6. What is Cognitive AI
Cogni&ve Ar&ficial Intelligence is part of MLaaS and is focused on:
• Vision Image to:
• Detect & Recognize Face; Recognize & Track Objects; Recognize Landmarks; Extract Text from
images & files; Iden/fy images on WWW; …*
• Vision Video to:
• Detect Labels, Faces; Track Person(s); …*
• Audio to:
• Speech-to-Text: Transcribe; Diarize; Recognize; Text-to-Speech; …*
• Language to:
• Translate; Detect Language; Detect Sen/ment; …*
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* New services and applica.on of services are con.nuing to be developed by the vendors

7. What is MLaaS
• Machine learning as a service (MLaaS)
• Umbrella definition of various cloud-based platforms that cover most infrastructure issues
such as:
1. data pre-processing
2. model training
3. model evaluation
4. inference (predictions using trained and deployed model)
• Inference (prediction) results can be bridged with the organizations "internal" IT
infrastructure programmatically through APIs (open or private connection), such as:
• REST through HTTPS/TLS*
• Subroutine libraries provided for different languages and programming frameworks
• C, C++, C#, Python, Golang, .NET etc depending on vendor
• Integration with vendors other services for NoOps, FaaS, IaaS, PaaS, SaaS
• Ordinarily over public Internet with HTTPS, but can also be over private connection ingress
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* Representa.onal state transfer (REST), h#ps://en.wikipedia.org/wiki/Representa.onal_state_transfer
* Hypertext Transfer Protocol (HTTP), h#ps://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol
* Transport Layer Security (TLS), h#ps://en.wikipedia.org/wiki/Transport_Layer_Security

8. What is Differentiating Cognitive AI MLaaS Vendors
• Vendors compete with their services based on efficacy, u&lity and outcome
• Employing top minds [unicorns] to enhance, improve and leap-frog compe/tors
• Differen&a&ng so^ware and special purpose hardware
• Differen&a&ng affinity of their respec/ve Point of Delivery (PoD) for accessing their services
• Cloud Vendor Network Peering (peering)* interconnect alterna<ves for Client on-premises
networks to connect directly into the Vendors cloud over a private connecDon facilitated by a
connecDvity provider, which significantly reduce network latency, security exposure and increase
connec<on reliability, not traversing the public Internet.
• Investments [huge] into:
• Staff & Resources for Research and Development
• Developing new CogniDve AI models
• Op<mizing exisDng CogniDve AI models
• Training models for CogniDve AI with vast datasets requiring unimaginable resources
• Data Centers (DC) globally, Internet ConnecDvity Peering or in country Point of Delivery (PoD)
presence for satellite locaDons or connecDvity providers
* Amazon Web Services h"ps://aws.amazon.com/directconnect/
* Microso2 Azure h"ps://docs.microso6.com/en-us/azure/expressroute/expressroute-introduc8on
* Google Cloud Pla:orm h"ps://cloud.google.com/interconnect/docs/concepts/overview
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9. Google Cloud PlaPorm
Cloud Vendors Global Networks or Local DC
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Amazon Web Services
MicrosoS Azure

10. ThousandEyes
Cloud Performance Benchmark 2019–2020
• "The 2019 Cloud Performance Benchmark measures and
compares network performance between five top public
cloud providers: Amazon Web Services (AWS), MicrosoI
Azure (Azure), Google Cloud PlaLorm (GCP), Alibaba Cloud
and IBM Cloud. The measurements gathered benchmark
the cloud providers against each other to discover what
consOtutes average, normaOve and best-in-class network
performance.”
• hRps://www.thousandeyes.com/resources/cloud-
performance-benchmark-report-november-2019
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11. December 2019 @ Dubai Data Science Fest
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Approach for rapid
Business
Impact
1. Business Needs
2. Cogni0ve Ar0ficial Intelligence
3. Outcomes

12. * "U.lity," Wikipedia, The Free Encyclopedia, h#ps://en.wikipedia.org/w/index.php?.tle=U.lity&oldid=914487837 (accessed September 14, 2019)
Business Needs
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utility
U)lity is the state of being useful,
profitable, or beneficial *

13. * "Efficacy," Wikipedia, The Free Encyclopedia, h#ps://en.wikipedia.org/w/index.php?.tle=Efficacy&oldid=912078824 (accessed September 14, 2019)
Business Needs
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efficacy
Efficacy is the ability to get a job done
sa)sfactorily *

14. * "Outcome (game theory)," Wikipedia, The Free Encyclopedia, h#ps://en.wikipedia.org/w/index.php?.tle=Outcome_(game_theory)&oldid=876148993 (accessed September 14, 2019)
Business Needs
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outcome
Outcome is the way things turns out; a
result or effect of an ac)on, situa)on; *
a situa)on which results from ac)ons

15. A practical approach – utility, efficacy & outcomes
• Initiate for utility
• Value Proposition with realization focus
• Structure for efficacy
• Phase implies time constrained project
• 3-sprint limit paradigm
1. Establish (resources)
2. Build (minimum viable product)
3. Demo (go/no-go for next)
• Bursting for outcomes
1. New Idea Proposal Phase (NIP) - Design Thinking
2. Rapid Accelerated Prototype Phase (RAPP) - sequential
3. Pilot Operationalize Phase (POP) - parallel
4. Rework & Integrate Phase (RIP) - prioritization
• Grinding to continue
• Govern, Operate, Prioritize, Heuristic, Execution & Realization (GOPHER)
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16. Value
Proposition
Current state
Desired state
Business solution
Funding (investment & financing)
Ac&ons to Realize
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17. Design Thinking Approach
• Empathize
• Understand the problem
• Define
• Analyze, Interpret & Plan
• Ideate
• Imagine, Research & Discuss
• Prototype
• Apply Crea/vity to Create
• Test
• Review Feedback to Revise
• Implement
• Realize, Follow-up & Assess
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18. Review
Data Strategy
implications
for
Information Lifecycle
Management, Ethics,
Privacy & Security
MLaaS – fast, quick & rapid outcome
Notebook – exploring "in-between"
Use Cases, such as with Open Source
models on your laptop or server, or
Google Colabs with TPUs
On-Premise In-House & Local –
iniFal exploraFon with actual but
curated data & what-if cases
December 2019 @ Dubai Data Science Fest
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19. Using Cloud Vendors
Cognitive AI MLaaS NoOps

20. Using Artificial Intelligence
• Applicability Decision Criteria (ADC)
• Why – is it needed
• What – is lacking in current
• When – is it needed
• Where – to integrate, in which application/solution/workflow
• How – to integrate
• Who – will staff the lifecycle: Plan*; Design; Develop; Deploy; Maintain & Govern;
• Selection Decision Criteria (SDC)
• Services
• Utility; Efficacy; Outcome (applicability; precision; accuracy; usefulness)
• Internet access point affinity; how close is the Point of Delivery (PoD) with network latency & reliability
• Commitment
• Inception & Actual track record; Release roadmaps; Governance program;
• Ecosystem integration;
• Support; Training (pull; push); Development;
• Development staffing; Open Research publication activity; Open Source sharing activity
• Cost & Lock-In
December 2019 @ Dubai Data Science Fest
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* "Ethics" is a critical topic area for inclusion in planning for Artificial Intelligence based solutions & systems.

21. Using the Cloud Vendors Cognitive AI MLaaS NoOps
Workflow:
1. Apply for an account
• AWS & GCP free for 12 months (some services always free to a cap level)
• Azure free for 1 month (some services always free to a cap level)
2. Prepare Request
3. Authenticate
4. Access API with Request
5. Postprocess the result from the Request
6. Curate & Use
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22. Vision Image – Cognitive Artificial Intelligence
• Face Detection* & Recognition
• Objects & Landmarks
• Web references
• Text OCR from images & files
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Amazon Microso, Google OpenCV
* Default settings applied to the Cloud Vendors services; for OpenCV some optimization to fit,
such as For detectMultiScale with scaleFactor=1.06, minSize=(30, 30), and
minNeighbors=4
* h#ps://www.nih.gov/sites/default/files/news-events/research-ma#ers/2014/20140428-a#en.on.jpg

23. Vision Image – Cognitive Artificial Intelligence
• Now this is how easy you do it the NoOps-way for Vision Image Face Detec)on &
Recogni)on; Objects & Landmarks; Web references; Text OCR from images & files
• Amazon
• heps://gist.github.com/realBjornRoden/3e4974baaf4848928e6d8224adb49bb1#cogni/ve-
ac/ons-vision-image-aws-md
• Google
• heps://gist.github.com/realBjornRoden/c46242be467066966c0da4c6166b6efa#cogni/ve-
ac/ons-vision-image-gcp-md
• MicrosoW
• heps://gist.github.com/realBjornRoden/a4c4f8c99851b9dq23e70d6fe37d348#cogni/ve-
ac/ons-vision-image-azure-md
December 2019 @ Dubai Data Science Fest
© Björn Rodén 25

24. Vision Video – Cognitive Artificial Intelligence
• Detect Labels
• Faces
• Track Person(s)
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© Björn Rodén 26
{
"JobStatus": "SUCCEEDED",
"VideoMetadata": {
"Codec": "h264",
"DurationMillis": 21656,
"Format": "QuickTime / MOV",
"FrameRate": 29.9689998626709,
"FrameHeight": 240,
"FrameWidth": 320
},
"Persons": [
{
"Timestamp": 1301,
"Person": {
"Index": 0,
"BoundingBox": {
"Width": 0.078125,
"Height": 0.22499999403953552,
"Left": 0.9125000238418579,
"Top": 0.30000001192092896
}
}
},
...
* h#ps://videos.cctvcamerapros.com/videos/15fps-surveillance-video.mp4

25. Vision Video – Cognitive Artificial Intelligence
• Now this is how easy you do it the NoOps-way for Vision Video Detect Labels,
Faces; Track Person(s)
• Amazon
• heps://gist.github.com/realBjornRoden/76af9b9dd1dfd80339ee5f6f8ac0dc3b#cogni/ve-
ac/ons-vision-video-aws-md
• Google
• heps://gist.github.com/realBjornRoden/c46242be467066966c0da4c6166b6efa#cogni/ve-
ac/ons-vision-video-gcp-md
• MicrosoW
• heps://gist.github.com/realBjornRoden/a4c4f8c99851b9dq23e70d6fe37d348#cogni/ve-
ac/ons-vision-video-azure-md
December 2019 @ Dubai Data Science Fest
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26. Audio – Cognitive Artificial Intelligence
• Speech-to-Text
• Transcrip/on
• Diariza/on
• Recogni/on
• Text-to-Speech
December 2019 @ Dubai Data Science Fest
© Björn Rodén 28
{
"results": [
{
"alternatives": [
{
"confidence": 0.96501887,
"transcript": "checking in with another show for HPR in the
car on my way to a client's going to be a short show I'm think I'm
going to be there in 10 minutes but I want to do you know shoot
something up the flagpole you're wanted to talk about the state of
podcasting these days these days I sound old because in podcasting
terms I am I've been around since 2004 mm started producing show since
2005 and have been listening to podcast daily since 2004 I came across
my archives from shows that I used to download back then and listen to
which I had burned to a CD and put them on my nose and I've started
streaming them while at work the last couple of weeks and I've had a
ball listening to old podcast episodes"
}
]
}
]
}
* h#ps://ia803009.us.archive.org/29/items/hpr2798/hpr2798.wav

27. Audio – Cognitive Artificial Intelligence
• Now this is how easy you do it the NoOps-way for Audio Transcrip)on;
Diariza)on; Recogni)on
• Amazon
• heps://gist.github.com/realBjornRoden/55e1b14a4fd6ecdfc64dbe7e8b95b15e#cogni/ve-
ac/ons-audio-aws-md
• Google
• heps://gist.github.com/realBjornRoden/3a2975556b4f3abb606577d87fee4234#cogni/ve-
ac/ons-audio-gcp-md
• MicrosoW
• heps://gist.github.com/realBjornRoden/a4c4f8c99851b9dq23e70d6fe37d348#cogni/ve-
ac/ons-audio-azure-md
December 2019 @ Dubai Data Science Fest
© Björn Rodén 29

28. Language – Cognitive Artificial Intelligence
• Transla)on
• Sen)ment
December 2019 @ Dubai Data Science Fest
© Björn Rodén 30
Google is using deepfakes to fight deepfakes. With the 2020 US
presidentia election approaching, the race is on to figure out
how to prevent widespread deepfake disinformation. On Tuesday,
Google offered the latest contribution: an open-source database
containing 3,000 original manipulated videos. The goal is to help
train and test automated detection tools. The company compiled
the data by working with 28 actors to record videos of them
speaking, making common expressions, and doing mundane tasks. It
then used publicly available deepfake algorithms to alter their
faces.
US English text OCR
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‫ﺎ‬
‫د‬
‫ﯾ‬
‫ﺔ‬
.
‫ﺛ‬
‫م‬
‫ا‬
‫ﺳ‬
‫ﺗ‬
‫ﺧ‬
‫د‬
‫م‬
‫ﺧ‬
‫و‬
‫ا‬
‫ر‬
‫ز‬
‫ﻣ‬
‫ﯾ‬
‫ﺎ‬
‫ت‬
deepfake
‫ا‬
‫ﻟ‬
‫ﻣ‬
‫ﺗ‬
‫ﺎ‬
‫ﺣ‬
‫ﺔ‬
‫ﻟ‬
‫ﻠ‬
‫ﺟ‬
‫ﻣ‬
‫ﮭ‬
‫و‬
‫ر‬
‫ﻟ‬
‫ﺗ‬
‫ﻐ‬
‫ﯾ‬
‫ﯾ‬
‫ر‬
‫و‬
‫ﺟ‬
‫و‬
‫ھ‬
‫ﮭ‬
‫م‬
.
Arabic translated
Google использует Deepfakes для борьбы с DeepFake. С приближением
президентских выборов в США в 2020 году начнется гонка, чтобы
выяснить, как предотвратить широкую фальшивую дезинформацию. Во
вторник Google предложил последнииA вклад: базу данных с открытым
исходным кодом, содержащую 3000 оригинальных манипулированных видео.
Цель состоит в том, чтобы помочь обучить и протестировать
автоматизированные инструменты обнаружения. Компания собрала данные,
работая с 28 актерами, чтобы записать видео, на которых они выступали,
делали общие выражения и выполняли повседневные задачи. Затем он
использовал общедоступные алгоритмы DeepFake, чтобы изменить их лица.
Russian translated

29. Language – Cognitive Artificial Intelligence
• Now this is how easy you do it the NoOps-way for Language Transla)on;
Sen)ment
• Amazon
• heps://gist.github.com/realBjornRoden/0afcfe61247efed998e937af4beb2537#cogni/ve-
ac/ons-language-aws-md
• Google
• heps://gist.github.com/realBjornRoden/8a4339299ff2812fd5769eab66fcea8e#cogni/ve-
ac/ons-language-gcp-md
• MicrosoW
• heps://gist.github.com/realBjornRoden/a4c4f8c99851b9dq23e70d6fe37d348#cogni/ve-
ac/ons-language-azure-md
December 2019 @ Dubai Data Science Fest
© Björn Rodén 31

30. OpenCV DNN with Mask R-CNN on Jupyter Notebook
December 2019 @ Dubai Data Science Fest
© Björn Rodén 32
$ virtualenv .
$ source ./bin/activate
$ pip install -r requirements.txt
$ export TF_CPP_MIN_LOG_LEVEL=2
$ jupyter notebook &
[I 19:54:47.259 NotebookApp] The Jupyter Notebook is running at:
[I 19:54:47.259 NotebookApp]
http://localhost:8888/?token=898d3e30407a0c9d0f8908cc0369c0d4116289cedc05ffd1
[I 19:54:47.259 NotebookApp] or
http://127.0.0.1:8888/?token=898d3e30407a0c9d0f8908cc0369c0d4116289cedc05ffd1
…
* Kudos to Anudeep Sekhar, h#ps://github.com/anudeepsekhar/The-Assembly-Computer-Vision-Workshop
* Google Tensorflow, h#ps://github.com/tensorflow/models/tree/master/research/object_detec.on
* Mask R-CNN paper on Arxiv, h#ps://arxiv.org/abs/1703.06870
* Analy.cs Vidhya, h#ps://www.analy.csvidhya.com/blog/2019/07/computer-vision-implemen.ng-mask-r-cnn-image-segmenta.on/
* Ma#erport, h#ps://github.com/ma#erport/Mask_RCNN
* COCO (Common Objects in Context), h#p://cocodataset.org/
* OpenCV at Embedded Vision Alliance, h#ps://www.embedded-vision.com/academy/Embedded_Vision_Alliance_Meetup_March_2019_OpenCV.pdf
*
CLASSES to detect:
person
bicycle
car
motorcycle
bus
train
truck
…

31. Finding models
December 2019 @ Dubai Data Science Fest
© Björn Rodén 33
h>ps://sotabench.com/user/ppwwyyxx/repos/tensorpack/tensorpack/27
Model Zoo’s and
benchmarked models
at sotabench.com by
Papers With Code

32. Next steps –a practical approach
• Ini&ate for u)lity
• Value Proposi-on with realiza/on focus
• Structure for efficacy
• Phase implies /me constrained project
• 3-sprint limit paradigm
1. Establish (resources)
2. Build (minimum viable product)
3. Demo (go/no-go for next)
• Burs&ng for outcomes
1. New Idea Proposal Phase (NIP) - Design Thinking
2. Rapid Accelerated Prototype Phase (RAPP) - sequen/al
3. Pilot Opera-onalize Phase (POP) - parallel
4. Rework & Integrate Phase (RIP) - priori/za/on
• Grinding to con)nue
• Govern, Operate, Priori/ze, Heuris/c, Execu/on & Realiza/on (GOPHER)
December 2019 @ Dubai Data Science Fest
© Björn Rodén 34

33. Tack Thanks Merci Grazie Gracias Obrigado Danke
ευχαριστώ Kösz Teşekkürler Спасибо ‫ﺷ‬
‫ﻜ‬
‫ﺮ‬
‫ا‬ Dankie
አመሰግናለሁ(ध"यवाद 谢谢 ありがとう
Questions?
December 2019 @ Dubai Data Science Fest
© Björn Rodén 35

34. Follow my Action Projects on github.io