Cloud and Industry4.0

Cloud and Industry 4.0 –
A Match made in the heaven
ganesh.vigneswara@gmail.com, ni_ganesh@cb.amrita.edu
Dr Ganesh Neelakanta Iyer
Amrita Vishwa Vidyapeetham
Associate Professor, Dept of Computer Science and Engg
Amrita School of Engineering, Coimbatore

About Me • Associate Professor, Amrita Vishwa Vidyapeetham
• Masters & PhD from National University of Singapore (NUS)
• Several years in Industry/Academia
• Architect, Manager, Technology Evangelist, Visiting Faculty
• Talks/workshops in USA, Europe, Australia, Asia
• Cloud/Edge Computing, IoT, Software Engineering, Game
Theory, Machine Learning
• Kathakali Artist, Composer, Speaker, Traveler, Photographer
GANESHNIYER http://ganeshniyer.com

Agenda
Introduction
Challenges of today’s world
Industry 4.0
Cloud Computing
Cloud and Industry 4.0
Fog and Edge Computing
Technology enablers of
Industry 4.0
ML and DL with Cloud
Services
Platforms
Infrastructure
Robotics and Cloud
IoT/IIoT and Cloud
Challenges and Best
Practices

DISCLAIMER
• Materials in this slides are taken with the help of google.
Due credit of the materials goes to the original people
• For all guys who are forced to be here today, please enjoy
Dilbert cartoons and pictures of countries I have been
• No MATHEMATICAL Formula in this 250 slide deck. Deal?


The Challenges of today’s world
Slides credit:
Fred Streefland
Cyber Security Strategist EMEA
Paloalto Networks

INSTRUMENTED & INTERCONNECTED
WORLD

HIGHLY AUTOMATED ADVERSARY
DIVERSE, EVOLVING AND
SOPHISTICATED THREAT

SOPHISTICATED MALWARE SPREADING
1 minute = 2,021 instances
15 minutes = 9,864 instances
30 minutes = 45,457 instances
New infection every 3 seconds
After….

12 | © 2017, Palo Alto Networks. All
Rights Reserved.
HIGHLY AUTOMATED ADVERSARIES

https://www.rmit.edu.au/content/dam/rmit/rmit-images/industry/industrial-revolutions2.png

https://www.nikunjbhoraniya.com/2019/03/industry-40-industrial-revolution.html

http://www.synergystix.com/wait-muscles-are-organs/
Automation and robotics
provide the muscle
Cloud is anywhere
everywhere
Data and connectivity are
its central nervous system
The brains behind this industrial
revolution is AI
AR/VR, cameras and other
sensors provide the senses

Cloud Computing - A vision to reality
Three decades ago, John Gage
(Sun Microsystems) made the
prophetic statement that:
“The network is the computer.”
Twenty-five years later, the advent
of Cloud Computing has finally
made this a reality.
Dr Ganesh Neelakanta Iyer 20
http://www.tmforum.org/CloudServicesBrokerage/10617/home.html
http://cloudcomputingcompaniesnow.com
http://archive.opengroup.org/public/member/q400/gage.jpg

Definition of Cloud Computing
21
NIST defines Cloud Computing as1: “Cloud computing is a model for
enabling ubiquitous, convenient, on-demand network access to a
shared pool of configurable computing resources (e.g., networks,
servers, storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort or service
provider interaction.”
[1] P. Mell and T. Grance. The NIST definition of cloud computing. NIST Special Publication 800-145, 2011.
http://cloudcomputingcompaniesnow.com/

http://www.tmforum.org/CloudServicesBrokerage/10617/home.html
Business Model: Conventional vs Cloud

Some examples from your daily life….

Cloud Delivery Models....
Software as a Service
(SaaS)
Platform as a Service
(PaaS)
Infrastructure as a Service
(IaaS)
CloudServiceModels
A software distribution model in which applications are hosted
by a service provider and made available to customers over
Internet
A way to rent resources (e.g. hardware, operating systems etc)
over the Internet. The service delivery model allows the customer
to rent virtualized servers and associated services for running
existing applications or developing and testing new ones.
A provision model in which an organization outsources the
equipment used to support operations, including storage,
hardware, servers and networking components.

SaaS
• Software as a service
• Ready made software
which can be altered to
suit your requirements
• Often delivered from a
public server (public
cloud)

SaaS: Starbucks
Starbucks wanted to know what
customers think about them
• Wanted a quick customized
CRM application
• Starbucks used Salesforce's
Force.com service to quickly
build out websites that tie into
new customer campaigns, as
the coffee giant attempts to
transform it business

What is driving the move to SaaS?
Market dynamics and disruptive technologies are driving the shift to SaaS consumption models
Developers want
Lines of Business want CxOs want
Low touch, easy to consume,
continuously updated software
SocialMobile
Embedded Intelligence
Cloud
Big Data
Predictability
Lower costs
Quicker business value
Access from anywhere
To create new offerings by
composing services from
multiple providers
IT Operations wants
To manage on-premise, Cloud, and hybrid environments

IaaS
• Raw infrastructure provided
to users
– Compute resources
– Storage
– Database
• Users can do whatever they
want to on that IaaS offering

IaaS: Netflix
• Needed an infrastructure to manage heavy lifting – Off load all
infrastructure complexity
• AWS helped achieve scalability, productivity, adapt to new features
• Netflix now: 86M users, 190 countries, 150M hours of streaming per day,
3 AWS regions and 12 availability zones, 100,000+ AWS instances

PaaS
• Platform as a service (PaaS) is a complete development and
deployment environment in the cloud, with resources that enable you
to deliver everything from simple cloud-based apps to sophisticated,
cloud-enabled enterprise applications
• You purchase the resources you need from a cloud service
provider on a pay-as-you-go basis and access them over a secure
Internet connection
• Like IaaS, PaaS includes infrastructure—servers, storage and
networking—but also middleware, development tools, business
intelligence (BI) services, database management systems and more
• PaaS is designed to support the complete web application lifecycle:
building, testing, deploying, managing and updating

PaaS: Dominos
For Dominos, already more than 60 per cent of
orders come through the online system. Scalability
and availability are crucial
• Underpinning that is a highly scalable, robust, reliable platform that can be deployed right around the
world, reaching each and every customer wherever they are and whenever they want us
• All their core business systems – their digital ordering systems, Dynamics ERP, back office
operations and supply chain systems – are in Microsoft cloud platform.

Cloud Delivery Models in a nutshell
Hosted
applications/apps
Development tools,
database management,
business analytics
Operating systems Servers and storage Networking
firewalls/security
Data center physical
plant/building

1. On-demand self service
• Cloud computing resources can be
provisioned without human interaction
from the service provider
• In other words, a customer can
provision additional computing
resources as needed without going
through the cloud service provider
• This can be a storage space, virtual
machine instances, database
instances, and so on

2. Broad network access
• Capabilities are available
over the network and
accessed through
standard mechanisms that
promote use by
heterogeneous thin or
thick client platforms (e.g.,
mobile phones, tablets,
laptops, and workstations)
• Network bandwidth and
latency are very important

3. Multi-tenancy and resource pooling
• Multi-tenancy allows multiple
customers to share the same
applications or the same physical
infrastructure while retaining privacy
and security over their information
• Resource pooling means that
multiple customers are serviced from
the same physical resources
• Providers' resource pool should be
very large and flexible enough to
service multiple client requirements
and to provide for economy of scale.

4. Rapid elasticity and scalability
• Ability to quickly provision
resources in the cloud as
customer need them
• And then to remove them when
they don't need them
• Cloud computing resources can
scale up or down rapidly and, in
some cases, automatically, in
response to business demands
• Elasticity means rapidly provision
and de-provision any of the cloud
computing resources

5. Measured Service
• Ability to quickly provision
resources in the cloud as
customer need them
• And then to remove them when
they don't need them
• Cloud computing resources can
scale up or down rapidly and, in
some cases, automatically, in
response to business demands
• Elasticity means rapidly provision
and de-provision any of the cloud
computing resources

Deployment
deployment
dɪˈplɔɪm(ə)nt/
Noun
1. the movement of troops or equipment to a place or position for military
action.
"the authorities announced deployment of extra security forces in towns and
cities to prevent violence"
2. the action of bringing resources into effective action.
"the rapid deployment of high-speed cable Internet services to consumers"

Software Deployment
• Software deployment is all of the
activities that make a software
system available for use
– Get the software out to the
customers
– Creating Installation Packages
– Documentation – Installation Guide
etc
• Deployment strategies may vary
depending of what kind of
software we create (Web,
Desktop, Mobile), , etc.

Cloud Deployment models
• Cloud allows you to deploy your applications in multiple
ways

Public Cloud
• The public cloud is defined as computing services offered by
third-party providers over the public Internet, making them
available to anyone who wants to use or purchase them
• They may be free or sold on-demand, allowing customers to
pay only per usage for the CPU cycles, storage or bandwidth
they consume
• Using public cloud services generates the types of economies
of scale and sharing of resources that can reduce costs and
increase choices of technologies.

Public Cloud - Features
• Cloud is open to the wide public
• Offers solutions for minimizing IT infrastructure costs
• Multi-tenancy is key
• A public cloud can offer any kind of services
• Most likely one or more datacenters constitutes the
physical infrastructure
• Pay as you use

Public Cloud - concerns
• Loss of control – Provider has full control on the
infrastructure and the data lying there
• Security
• Regulatory issues

Private Cloud
• Virtual distributed systems that rely on a private
infrastructure and provide internal users with dynamic
provisioning of computing resources
• Core business operations are in-house
• Key advantages
– Customer information protection
– Infrastructure ensuring SLAs
– Compliance with standard procedures and operations
• Major drawback – Inability to scale elastically on-demand

Virtual Private Cloud
• On-demand configurable pool of
shared computing resources
allocated within a public cloud
environment, providing a certain
level of isolation between the
different organizations using the
resources
• In a VPC, providing isolation within
the cloud, is accompanied with a
VPN function that secures, by
means of authentication and
encryption, the remote access of the
organization to its VPC cloud
resources

Community Cloud
• A community cloud is a cloud
service model that provides a
cloud computing solution to a
limited number of individuals or
organizations that is governed,
managed and secured
commonly by all the
participating organizations or a
third party managed service
provider

Examples
• QTS Healthcare Community Cloud
• Healthcare Community Cloud provides
a solution for people at different
endpoints to access this information
conveniently and securely
• Physician groups, hospitals, health plan
administrators, healthcare
clearinghouses, and other members of
the healthcare community are
revolutionizing the way they collaborate
via the cloud
• The Northwest Regional Data Center
• Established in 1972, NWRDC initially
offered mainframe services to
universities across the state as
a community cloud system
• A self-governance model makes
NWRDC a computing cooperative of
over 70 member orgs with access to
enterprise-level services and
facilities that would be difficult and
expensive to implement individually
• Still heavily rooted in education,
NWRDC now provides services to a
wide range of universities, colleges, and
state, county, and city governments
https://www.qtsdatacenters.com/resources/blog/2016/05/1
8/introducing-healthcare-community-cloud
https://er.educause.edu/articles/2015/8/a-
community-cloud-the-northwest-regional-data-
center

Hybrid Cloud
• A hybrid cloud is a computing environment
which combines a public cloud and
a private cloud by allowing data and
applications to be shared between them
• When computing and processing demand
fluctuates, hybrid cloud computing gives
businesses the ability to seamlessly scale
their on-premises infrastructure up to the
public cloud to handle any overflow -
without giving third-party datacenters
access to the entirety of their data
• Organisations gain the flexibility and
computing power of the public cloud for
basic and non-sensitive computing tasks,
while keeping business-critical applications
and data on-premises, safely behind a
company firewall.

Cloud deployment models

Cloud and Industry 4.0
• No matter what industry you’re in, cloud technology is a
critical enabler of the next Industrial Revolution, by providing
the means for businesses to innovate around these
technologies
– Pascal Giraud, Oracle EMEA
• The Cloud is the connective tissue of Industrie 4.0, the key
element that makes it possible to develop a production
strategy that is innovative, more effective and effcient by
leveraging sensors, artificial intelligence and robotics
– Reply Red, Consultants, UK

ROI on cloud projects
Increased business flexibility and agility
• The Cloud makes it possible to scale computing power, as well as network and storage capacity, with ease, guaranteeing that infrastructural
elasticity which allows the company to cope with sudden peaks in activity
Increased operational efficiency
• Zero deployment time, with a significant reduction in operational activities and infrastructure maintenance.
Shorter innovation cycles
• Constant updating and continuous improvement of services related to the Cloud platforms, with the guarantee of maximum simplification
of the IT infrastructure.
Cost reduction
• The reduction of IT infrastructure management costs (power, UPS devices, connectivity, airconditioning, staff, etc.) and simplified maintenance

Cloud Manufacturing
• process of utilizing well established
manufacturing resources, such as Enterprise
Resource Planning (ERP), through the cloud
• This way, the information can be viewed,
updated and applied at any time or place
• Cloud manufacturing was intended to handle
“big manufacturing” which means it follows the
whole manufacturing process from the
designing stage to production to maintenance
• It incorporates other key technologies such as
Industrial IoT (IIoT), CPS etc
https://erpsoftwareblog.com/cloud/2016/06/what-is-cloud-manufacturing/ |
ERP Cloud Blog

Cloud Manufacturing
• Companies can already begin to envision
production not as a process, but as a genuine
service
• in the not too distant future, it will be possible to
use
– virtual plants (simple 3D printers or new generation
numerical control machines),
– located strategically close to the target consumers
(thereby reducing investments in inventory) and
– reducing the production capacity to capitalise on
sales results quickly and to adapt to changing
market conditions with flexibility
https://erpsoftwareblog.com/cloud/2016/06/what-is-cloud-manufacturing/ |
ERP Cloud Blog

Edge Computing
• Edge computing is a
method of optimizing
cloud computing systems
"by taking the control of
computing applications,
data, and services away
from some central nodes
(the "core") to the other
logical extreme (the
"edge") of the Internet"
which makes contact with
the physical world -
Wikipedia

Fog Computing
• Fog computing pushes intelligence
down to the local area network (LAN)
level of network architecture, processing
data in a fog node or IoT gateway
• Edge computing pushes the
intelligence, processing power, and
communication capabilities of an edge
gateway or appliance directly into
devices
• Cisco created the term fog computing
years ago to describe a layer of
computing at the edge ofthe network
that could allow pre-processed data to
be quickly and securely transported to
the cloud.

Need for FOG/EDGE
• The shop floor and the assembly line are becoming
increasingly more connected
• The number of devices, such as 3D cameras, new-
generation numerical control machines and various kinds
of sensors that generate data in real time to ensure a
more efficient productive process, are actively increasing
• Internet networks are increasingly more congested and it
is impossible to reprocess salient information in a short
period of time

Enabling technologies for EDGE
Cloud
Computing
Sensors and
Intelligent
objects
5G Wireless
networks
M2M
Connections

Edge + Cloud
• An integrated system to run different applications very close to production
• Also connected to cloud for management of applications, remote updates
67
https://www.cleantech.com/energy-power-
shifts-from-iot-cloud-to-edge-computing/

Edge + Cloud
• Say you had a pattern detector that triggers an alert if there’s a rapid
rise in (equipment) temperature
– EDGE Computing - You can handle that locally by going into the control
and changing some parameters or notifying some other system on the
plant floor
• If you want to compare the average temperature of every red
machine in a plant to red machines located everywhere in the world,
each red machine at the edge computes its average temperature,
sends that result to the cloud, and then the cloud sends down the
average of all the machines to the edge
– CLOUD Computing – Aggregate Analytics
https://www.ctemag.com/news/articles/industry-40-
advantages-edge-computing

Edge Computing and its relevance to
Industry 4.0
Edge computing will keep you safe
• Industry 4.0 is all about connecting machines, so your manufacturing processes can react more quickly and
intelligently to changing factory floor conditions
Edge computing will make your Big Data small
• Bringing intelligence to your manufacturing operations means collecting data from sensors in your equipment and
analyzing data to make real-time decisions and predictive maintenance
Edge computing will give you ultra-low latency
• With edge computing, you can easily connect machines from different manufacturers with an independent and
resilient logic layer running local triggers ultra-fast
Edge computing can be the integration layer between your factory floor data and your ERP
system
• Edge computing can be the real-time, event-driven integration layer between your factory floor data and your
enterprise systems that will help you speed up and automate business processes and digital insights
Dr Ganesh Neelakanta Iyer 69https://iiot-world.com/connected-industry/4-0-reasons-why-edge-computing-is-relevant-for-industry-4-0/

Building The Intelligent Supply Chain
• The Internet of Things (IoT) makes business applications interact with the
physical world
• Big Data makes large data sets accessible for advanced analytics and
intelligence
• Machine learning (ML) and artificial intelligence (AI) automate repetitive
processes and learn from human exception handling and decision-making
• Advanced analytics finds data patterns to support decisions and predict
the future
• Blockchain distributes collaborative processes across the entire value
network
• Data intelligence finds new value in data assets for new business models

Enablers of Industry 4.0 and role of Cloud
• Cloud services for users with no ML knowledge
• Cloud platform services for expert ML guys
• Cloud Infra for deep learning
Artificial
Intelligence
• Task offloading to cloud – mobile robots
• Cloud based robotic services
• Knowledge sharing platform for robots via cloud
Robotics
• Data processing with Cloud
• Extend the processing to the edge
• IoT analytics
IoT

AI and ML
Dr Ganesh Neelakana Iyer

Artificial Intelligence
• “The study of the modelling of human mental functions by
computer programs.” —Collins Dictionary
Dr Ganesh Neelakanta Iyer 75https://medium.com/life-of-a-technologist/what-would-the-managers-manage-in-
the-age-of-ai-6a00c26df257

Artificial Intelligence
• AI is composed of 2 words Artificial and Intelligence
• Anything which is not natural and created by humans is artificial
• Intelligence means ability to understand, reason, plan etc.
• So any code, tech or algorithm that enable machine to mimic,
develop or demonstrate the human cognition or behavior is AI

Possible applications of AI
Dr Ganesh Neelakanta Iyer 77https://pbs.twimg.com/media/DUn4kQzXkAAaqGS.jpg

AI vs ML
http://godigitalcrazy.com/artificial-intelligence-machine-learning-data-analytics/

Machine Learning
Dr Ganesh Neelakanta Iyer 79https://towardsdatascience.com/machine-learning-65dbd95f1603

Why Machine Learning is Hard
You See Your ML Algorithm Sees

Why Machine Learning Is Hard, Redux
What is a “2”?

Why machine learning is hard?
Learning to identify an ‘apple’?
Apple Apple corporation Peach
Colour Red White Red
Type Fruit Logo Fruit
Shape Oval Cut oval Round
Slide credit: Edit

So much for a cat.
Principle of machine learning
Slide credit: Edit

General ML Framework

Two major types
https://blog.westerndigital.com/machine-learning-pipeline-object-storage/

Deep Learning

Deep Learning
• “Deep Learning is a subfield of machine learning
concerned with algorithms inspired by the structure and
function of the brain called artificial neural networks”
—Machine Learning Mastery

Deep Learning
• It’s a particular kind of machine
learning that is inspired by the
functionality of our brain cells called
neurons which lead to the concept
of artificial neural network(ANN)
• ANN is modeled using layers of
artificial neurons or computational
units to receive input and apply an
activation function along with
threshold
https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55

What is Deep Learning?
https://medium.com/swlh/ill-tell-you-why-deep-learning-is-so-popular-and-in-demand-
5aca72628780

AI vs ML vs DL
Dr Ganesh Neelakanta Iyer 90https://twitter.com/IainLJBrown/status/952846885651443712

Cloud-based Machine Learning Services
• Machine learning platforms are one of the fastest growing
services of the public cloud
• Unlike other cloud-based services, ML and AI platforms
are available through diverse delivery models such as
– cognitive computing
– automated machine learning
– ML model management
– ML model serving and
– GPU-based computing

ML and AI
spectrum in Cloud
• Like the original
cloud delivery
models of IaaS,
PaaS, and SaaS,
ML and AI
spectrum span
infrastructure,
platform and high-
level services
exposed as APIs
https://www.forbes.com/sites/janakirammsv/2019/01/01/an-executives-
guide-to-understanding-cloud-based-machine-learning-
services/#7fa721383e3e

Cognitive Services
• Cognitive computing is delivered as a set of APIs that offer computer
vision, natural language processing (NLP) and speech services
• Developers can consume these APIs like any other web service or
REST API
• Developers are not expected to know intricate details of machine
learning algorithms or data processing pipelines to take advantage
of these services
• As the consumption of these services rises, the quality of cognitive
services increases
• With the increase in data and usage of the services, cloud providers
are continually improving the accuracy of the predictions

Automated ML
• Developers can use the APIs after training the service
with custom data
• AutoML offers a middle ground to consuming pre-trained
models vs. training custom models from scratch
• From object detection to sentiment analysis, you will be
able to tap into readily available AI services
• Think of these APIs the SaaS equivalent of AI where you
only pay for what you use

Amazon Rekognition
https://aws.amazon.com/rekognition/
• Amazon Rekognition makes it easy to add image and video analysis to
your applications
• You just provide an image or video to the Rekognition API, and the service
can identify the objects, people, text, scenes, and activities, as well as
detect any inappropriate content.
• Amazon Rekognition also provides highly accurate facial analysis and
facial recognition on images and video that you provide.
• You can detect, analyze, and compare faces for a wide variety of user
verification, people counting, and public safety use cases

Amazon Rekognition
https://aws.amazon.com/rekognition/
• Amazon Rekognition is based on the same proven, highly scalable,
deep learning technology developed by Amazon’s computer vision
scientists to analyze billions of images and videos daily, and requires
no machine learning expertise to use
• Amazon Rekognition is a simple and easy to use API that can
quickly analyze any image or video file stored in Amazon S3.
• Amazon Rekognition is always learning from new data, and we are
continually adding new labels and facial recognition features to the
service

Key features
• Object, scene and activity detection

Key features
• Facial recognition

Key features
• Facial analysis

Key features
• Pathing

Key features
• Unsafe content detection

Key features
• Celebrity recognition

Key features
• Text in images

Amazon Rekognition Video

Google Cloud Vision API
https://cloud.google.com/products/ai/building-blocks/
• Cloud Vision offers both pretrained models via an API and the ability to
build custom models using AutoML Vision to provide flexibility depending
on your use case
• Cloud Vision API enables developers to understand the content of an
image by encapsulating powerful machine learning models in an easy-to-
use REST API
• It quickly classifies images into thousands of categories, detects individual
objects and faces within images, and reads printed words contained within
images
• You can build metadata on your image catalog, moderate offensive
content, or enable new marketing scenarios through image sentiment
analysis. Dr Ganesh Neelakanta Iyer 109

Google AutoML Vision
• AutoML Vision Beta makes it possible for developers
with limited machine learning expertise to train high-
quality custom models
• After uploading and labeling images, AutoML Vision will
train a model that can scale as needed to adapt to
demands
• AutoML Vision offers higher model accuracy and faster
time to create a production-ready model.

Characteristics
• Insight from your images
– Easily detect broad sets of objects in your images, from flowers,
animals, or transportation to thousands of other object categories
commonly found within images
– Vision API improves over time as new concepts are introduced and
accuracy is improved. With AutoML Vision, you can create custom
models that highlight specific concepts from your images
– This enables use cases ranging from categorizing product images to
diagnosing diseases

Characteristics
• Extract text
– Optical Character Recognition (OCR) enables you to detect text
within your images, along with automatic language
identification.
– Vision API supports a broad set of languages

Characteristics
• Power of the web
– Vision API uses the power of Google Image Search to find
topical entities like celebrities, logos, or news events
– Millions of entities are supported, so you can be confident that
the latest relevant images are available
– Combine this with Visually Similar Search to find similar images
on the web.

Characteristics
• Content moderation
– Powered by Google SafeSearch, easily moderate content and
detect inappropriate content from your crowd-sourced images
– Vision API enables you to detect different types of inappropriate
content, from adult to violent content.

Image search
Use Vision API and AutoML Vision to make images searchable across broad topics and
scenes, including custom categories.
https://cloud.google.com/solutions/image-search-app-with-cloud-vision/

Document classification
Access information efficiently by using the Vision and Natural Language APIs to transcribe and
classify documents.

Product Search
Find products of interest within images and visually search product catalogs using Cloud Vision API

Cloud Vision API features
Label
detection
Web detection
Optical
character
Handwriting
recognitionBETA Logo detection
Object
localizerBETA
Integrated
REST API
Landmark
detection
Face detection
Content
moderation
ML Kit
integration
Product
searchBETA
Image
attributes

How Auto-ML VisionBETA works

Attractive Pricing

Video Intelligence
• Google also assures the Video Intelligence to perform
video analysis, classification, and labeling
• This allows searching through the videos based on the
extracted metadata
• It is also possible to detect the change of the scene and
filter the explicit content.

Microsoft Computer Vision
• Extract rich information from images to categorize and
process visual data—and perform machine-assisted
moderation of images to help curate your services
• This feature returns information about visual content found in
an image
• Use tagging, domain-specific models, and descriptions in four
languages to identify content and label it with confidence
• Apply the adult/racy settings to help you detect potential adult
content
• Identify image types and color schemes in pictures

Microsoft Computer Vision
Analyze an
image
Read text in
images
Preview: Read
handwritten
text from
images
Recognize
celebrities and
landmarks
Analyze video
in near real-
time
Generate a
thumbnail

Microsoft Computer Vision - Pricing

ML Platform as a Service
• When cognitive APIs fall short of requirements, you can
leverage ML PaaS to build highly customized machine
learning models
• For example, while a cognitive API may be able to identify the
vehicle as a car, it may not be able to classify the car based
on the make and model
• Assuming you have a large dataset of cars labeled with the
make and model, your data science team can rely on ML
PaaS to train and deploy a custom model that’s tailormade for
the business scenario

• Similar to PaaS delivery model where developers bring their
code and host it at scale, ML PaaS expects data scientists to
bring their own dataset and code that can train a model
against custom data
• They will be spared from provisioning compute, storage and
networking environments to run complex machine learning
jobs
• Data scientists are expected to create and test the code with
a smaller dataset in their local environments before running it
as a job in the public cloud platform

• ML PaaS removes the friction involved in setting up and configuring data
science environments
• It provides pre-configured environments that can be used by data
scientists to train, tune, and host the model
• ML PaaS efficiently handles the lifecycle of a machine learning model by
providing tools from data preparation phase to model hosting
• They come with popular tools such as Jupyter Notebooks which are
familiar to the data scientists
• ML PaaS tackles the complexity involved in running the training jobs on a
cluster of computers
• They abstract the underpinnings through simple Python or R API for the
data scientists

• Simplify and accelerate the building, training and deployment of your ML models
• Use automated ML to identify suitable algorithms and tune hyperparameters faster
• Seamlessly deploy to the cloud and the edge with one click
• Access all these capabilities from your favourite Python environment using the latest
open-source frameworks, such as PyTorch, TensorFlow and scikit-learn

How to use Azure Machine Learning service
• Step 1: Creating
a workspace
• Install the SDK in
your favourite
Python
environment, and
create your
workspace to store
your compute
resources,
models,
deployments and
run histories in the
cloud.

• Step 2: Build and
train
• Use frameworks of
your choice and
automated
machine learning
capabilities to
identify suitable
algorithms and
hyperparameters
faster. Track your
experiments and
easily access
powerful GPUs in
the cloud.

• Step 3: Deploy and
manage
• Deploy models to the
cloud or at the edge
and leverage
hardware-
accelerated models
on field-
programmable gate
arrays (FPGAs) for
super-fast
inferencing. When
your model is in
production, monitor it
for performance and
data drift, and retrain
it as needed.

ML Infrastructure Services
• Think of ML infrastructure as the IaaS of the machine learning stack
• Cloud providers offer raw VMs backed by high-end CPUs and
accelerators such as graphics processing unit (GPU) and field
programmable gate array (FPGA)
• Developers and data scientists that need access to raw compute
power turn to ML infrastructure
• For complex deep learning projects that heavily rely on niche toolkits
and libraries, organizations choose ML infrastructure
• They get ultimate control of the hardware and software configuration
which may not be available from ML PaaS offerings

ML Infrastructure Services
• Recent hardware investments from Amazon, Google,
Microsoft and Facebook, made ML infrastructure cheaper and
efficient
• Cloud providers are now offering custom hardware that’s
highly optimized for running ML workloads in the cloud
• Google’s TPU and Microsoft’s FPGA offerings are examples
of custom hardware accelerators exclusively meant for ML
jobs
• When combined with the recent computing trends such as
Kubernetes, ML infrastructure becomes an attractive choice
for enterprises

Deep Learning Cloud Service Providers
# Name URL
1 Alibaba https://www.alibabacloud.com
2 AWS EC2 https://aws.amazon.com/machine-learning/amis
3 AWS Sagemaker https://aws.amazon.com/sagemaker
4 Cirrascale http://www.cirrascale.com
5 Cogeco Peer 1 https://www.cogecopeer1.com
6 Crestle https://www.crestle.com
7 Deep Cognition https://deepcognition.ai
8 Domino https://www.dominodatalab.com
9 Exoscale https://www.exoscale.com
10 FloydHub https://www.floydhub.com/jobs
11 Google Cloud https://cloud.google.com/products/ai
12 Google Colab https://colab.research.google.com
13 GPUEater https://www.gpueater.com
14 Hetzner https://www.hetzner.com
15 IBM Watson https://www.ibm.com/watson
16 Kaggle https://www.kaggle.com
https://towardsdatascience.com/list-of-deep-
learning-cloud-service-providers-579f2c769ed6

Deep Learning Cloud Service Providers
# Name URL
17 Lambda https://lambdalabs.com
18 LeaderGPU https://www.leadergpu.com
19 Microsoft Azure https://azure.microsoft.com
20 Nimbix https://www.nimbix.net
21 Oracle https://cloud.oracle.com
22 Outscale https://en.outscale.com
23 Paperspace https://www.paperspace.com
24 Penguin Computing https://www.penguincomputing.com
25 Rapid Switch https://www.rapidswitch.com
26 Rescale https://www.rescale.com
27 Salamander https://salamander.ai
28 Spell https://spell.run
29 Snark.ai https://snark.ai
30 Tensorpad https://www.tensorpad.com
31 Vast.ai https://vast.ai
32 Vectordash https://vectordash.com
https://towardsdatascience.com/list-of-deep-
learning-cloud-service-providers-579f2c769ed6

Need for AI in Industry 4.0
• Industrial companies
often have large
amounts of data
without generating
any added value from
it. According to a
study by the World
Economics Forum in
cooperation with A.T.
Kearney is currently
70% of all collected
production data is not
used

Need for AI in Industry 4.0
• The development of market-ready AI tools and the availability
of scalable computing power enable manufacturers to
integrate machine learning into their processes
• By using these self-learning algorithms, companies can gain
proactive insights into production and thus become more
competitive
• Machine-learning algorithms bring two major advantages to
the production process:
– Improvement of product quality
– Flexibility of the production process

Cloud Robotics
• Cloud robotics services that take the pain out of the robot
development lifecycle are a vital step forward on the path
to increased robot affordability and ease of development
Robots as a service (plus the cloud)
• Low capital expenditures plus mad robot capabilities! Hence the rise of robot
rentals—on-location at your business—with cloud-enabled, pay-as-you-go
services attached
Robots in the cloud
• Programming a remote physical robot that’s accessible over the cloud

Robots in the cloud
• Democratize robotics by providing remote access
to a state-of-the-art multi-robot research facility
• The Robotarium project provides a remotely accessible swarm robotics
research platform that remains freely accessible to anyone
• Currently, Robotics research requires significant investments in terms of
manpower and resources to competitively participate
• However, we believe that anyone with new, amazing ideas should be able
to see their algorithms deployed on real robots, rather than purely
simulated
• In order to make this vision a reality, we have created a remote-access,
robotics lab where anyone can upload and test their ideas on real robotic
hardware

Some newest Cloud Robotics platforms
AWS RoboMaker
• Integration of the open-source ROS framework with Amazon’s cloud-based machine learning services
Honda Robotics as a Service Platform
• Software platform (APIs/SDKs) for functions, such as collecting and sharing data, controlling
communication, changing states, and robotic cooperation
Google Cloud Robotics
• Collaborative robots, Solution for robots working at scale
Microsoft ROS for Windows
• The ROS for Windows provides your local robot with the benefits of Microsoft’s enterprise expertise
(Security, scalability) and cloud-based ML/AI services

Honda RaaS
Dr Ganesh Neelakanta Iyer 165https://global.honda/innovation/CES/2019/raas_platform.html

IoT / IIoT and Cloud

Evolution of Internet of Things
Dr Ganesh Neelakanta Iyer 167http://www.geocities.ws/cheps/internet.html

Source: https://www.i-scoop.eu/internet-of-things-guide/

Industrial IoT (IIoT)
• Plant data is collected and sent for processing to the cloud, a
data center containing a group of servers connected to the
internet
• This centralized data handling system gives users a global
view of all connected equipment, which may be in a number
of different locations
• The system also allows users to quickly and easily update
software in far-flung machines
• However, “if you push everything to the cloud, you are
dependent on network connectivity up to a cloud system,”

Industrial IoT (IIoT)

How IoT and Cloud complement each other?
171https://blog.resellerclub.com/what-is-the-role-of-cloud-computing-in-iot/

Why is Cloud essential to the success of
IoT?
Provides remote
processing power
• Cloud as a technology empowers IoT to move beyond
regular appliances such as air conditioners, refrigerators
etc
Provides security
and privacy
• It has enabled users with strong security measures by
providing effective authentication and encryption
protocols
Removes entry
barrier for hosting
providers
• Hosting providers do not have to depend on massive
equipment or even any kind of hardware that will not
support the agility IoT devices require
Facilitates inter-
device
communication
• Cloud acts as a bridge in the form of a mediator or
communication facilitator when it comes to IoT

IIoT and challenges with Cloud
Network
Connectivity
If the network breaks down, so do critical cloud-based production
applications.
Latency it takes time for data to travel back and forth between the cloud and the
plant floor, applications that require real-time responses cannot work
properly
Data Load Consider a shop floor with 50 machines, to be monitored. Need to
collect data hundreds of times per second by a large number of
sensors.
Privacy and
Security
Firms reluctant to push secret and essential data out of their shops,
where it could be more vulnerable to theft

In most ways, IoT analytics are like any
other analytics

What Makes IoT Analytics Different?

Need Advanced Analytics and Machines to
Help Manage It
• Just as the increase in data will push companies to “the
edge,” it will also push them toward AI and ML
• Indeed, AI will also become a necessity, as the amount of
data created by the IoT will simply be too large for
humans to manage
• We will see a strong growth in analytics software and
tools to provide real-time data streaming for IoT devices

Dr Ganesh Neelakanta Iyer 180https://www.accenture.com/in-en/internet-of-things-analytics

https://www.accenture.com/in-en/internet-of-things-analytics

Example - AWS IoT Analytics
• Fully-managed service for sophisticated analytics on
massive volumes of IoT data
• Eliminate the cost and complexity typically required to
build your own IoT analytics platform
• Run analytics on IoT data and get insights to make better
and accurate decisions for IoT and ML use cases
https://aws.amazon.com/iot-analytics/

AWS IoT Analytics
Dr Ganesh Neelakanta Iyer 183https://aws.amazon.com/iot/

IoT, Containers and Microservices

Flashback
Lets go back to pre-1960’s

Multiplicityof
Goods
Multiplicityof
methodsfor
transporting/storing
DoIworryabout
howgoodsinteract
(e.g.coffeebeans
nexttospices)
CanItransport
quicklyandsmoothly
(e.g.fromboatto
traintotruck)
Cargo Transport Pre-1960

? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
Also an M x N Matrix

Multiplicityof
Goods
Multiplicityof
methodsfor
transporting/storing
DoIworryabout
howgoodsinteract
(e.g.coffeebeans
nexttospices)
CanItransport
quicklyand
smoothly
(e.g.fromboatto
traintotruck)
Solution: Intermodal Shipping Container
…in between, can be loaded and
unloaded, stacked, transported
efficiently over long distances,
and transferred from one mode
of transport to another
A standard container that is
loaded with virtually any
goods, and stays sealed until
it reaches final delivery.

This eliminated the M x N problem…

and spawned an Intermodal Shipping Container Ecosystem
• 90% of all cargo now shipped in a standard container
• Order of magnitude reduction in cost and time to load and unload ships
• Massive reduction in losses due to theft or damage
• Huge reduction in freight cost as percent of final goods (from >25% to <3%) massive globalizations
• 5000 ships deliver 200M containers per year

Static website
Web frontend
User DB
Queue Analytics DB
Background workers
API endpoint
nginx 1.5 + modsecurity + openssl + bootstrap 2
postgresql + pgv8 + v8
hadoop + hive + thrift + OpenJDK
Ruby + Rails + sass + Unicorn
Redis + redis-sentinel
Python 3.0 + celery + pyredis + libcurl + ffmpeg + libopencv + nodejs +
phantomjs
Python 2.7 + Flask + pyredis + celery + psycopg + postgresql-client
Development VM
QA server
Public Cloud
Disaster recovery
Contributor’s laptop
Production Servers
The Challenge
Multiplicityof
Stacks
Multiplicityof
hardware
environments
Production Cluster
Customer Data Center
Doservicesand
appsinteract
appropriately?
CanImigrate
smoothlyand
quickly?

Results in M x N compatibility nightmare
Static website
Web frontend
Background workers
User DB
Analytics DB
Queue
Development
VM
QA Server
Single Prod
Server
Onsite
Cluster
Public Cloud
Contributor’s
laptop
Customer
Servers
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?
? ? ? ? ? ? ?

Static website Web frontendUser DB Queue Analytics DB
Development
VM
QA server Public Cloud Contributor’s
laptop
Docker is a shipping container system for
code
Multiplicityof
Stacks
Multiplicityof
hardware
environments
Production
Cluster
Customer Data
Center
Doservicesand
appsinteract
appropriately?
CanImigrate
smoothlyand
quickly
…that can be manipulated using
standard operations and run
consistently on virtually any
hardware platform
An engine that enables any
payload to be encapsulated
as a lightweight, portable,
self-sufficient container…

Static website Web frontendUser DB Queue Analytics DB
Development
VM
QA server Public Cloud Contributor’s
laptop
Or…put more simply
Multiplicityof
Stacks
Multiplicityof
hardware
environments
Production
Cluster
Customer Data
Center
Doservicesand
appsinteract
appropriately?
CanImigrate
smoothlyand
quickly
Operator: Configure Once, Run
Anything
Developer: Build Once, Run
Anywhere (Finally)

Static website
Web frontend
Background workers
User DB
Analytics DB
Queue
Development
VM
QA Server
Single Prod
Server
Onsite
Cluster
Public Cloud
Contributor’s
laptop
Customer
Servers
Docker solves the M x N problem

Virtualization vs Containers
https://blogs.msdn.microsoft.com/uk_faculty_connection/2016/09/23/gettin
g-started-with-docker-and-container-services/

Microservices
https://blog.golemproject.net/golem-microservices-
%EF%B8%8F-part-1-1f1ef7b9af29

IoT and Microservices
Low Cost
• Rely on microservices to add value and fill functional gaps; Gradually
roll out the network and continue to upgrade and maintain it in a cost-
effective manner as individual components get replaced
Faster Innovation
• A microservices development approach allows you to unlock innovation
and value faster by making it easy to test new combinations of “things”
and “services.”
• With microservices, you can tinker and test to your heart’s content and
quickly reap the benefits of innovative solutions to your problems.
http://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Five-
things-to-know-about-the-future-of-microservices-and-IoT Dr Ganesh Neelakanta Iyer 202

IoT and Microservices
Isolated Risk
• Assembling your solution via microservices allows you to adjust and iterate
quickly, thus avoiding the danger of missing the mark
Flexibility and Agility
• Assembling your solution via microservices allows you to adjust and iterate
quickly, thus avoiding the danger of missing the mark
Unlimited value-add
• The digital upgrades you can provide via constantly evolving microservices,
however, are unlimited both in their scope and their frequency
• A camera may be designed to only capture 2D images, but depending on the
third-party service it’s linked to, it might provide you with statistical traffic
information, queue sizes or weather information.
http://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Five-things-to-
know-about-the-future-of-microservices-and-IoT

Deploy Azure IoT Edge on a simulated
device in Windows - preview
https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-simulate-device-linux
Next slides are only for your reference; You can try it out by referring to the slides
or below URL at your spare time. It will help you understand the concepts in real.

Fully managed service that delivers cloud intelligence locally by deploying &
running AI, Azure services, and custom logic directly on cross-platform IoT devices
205
https://azure.microsoft.com/en-in/services/iot-edge/

Azure IoT Edge
 Next few slides help you to
• Create an IoT Hub
• Register an IoT Edge device
• Start the IoT Edge runtime
• Deploy a module
 The simulated device that you
create in this tutorial is a monitor
on a wind turbine that generates
temperature, humidity, and
pressure data. You're interested in
this data because your turbines
perform at different levels of
efficiency depending on the
weather conditions. Dr Ganesh Neelakanta Iyer 206

Prerequisites
• This tutorial assumes that you're using a computer or virtual
machine running Windows to simulate an Internet of Things device
• Make sure you're using a supported Windows version:
– Windows 10
– Windows Server
• Install Docker for Windows and make sure it's running
• Install Python 2.7 on Windows and make sure you can use the pip
command
• Run the following command to download the IoT Edge control script
– pip install -U azure-iot-edge-runtime-ctl

Create an IoT hub

• Sign in to
the Azure portal -
https://portal.azur
e.com/
• Sign up
• You need to
provide a valid
credit card details
– However it is
absolutely free
– They will change
Rs2/- to see if
your card is valid
• Select Create a
resource >
Internet of Things
> IoT Hub.

• In the IoT hub pane, enter the following
information for your IoT hub:
– Name: Create a name for your IoT hub. If the name
you enter is valid, a green check mark appears
– Pricing and scale tier: For this tutorial, select the F1 -
Free tier.
– Resource group: Create a resource group to host the
IoT hub or use an existing one.
– Location: Select the closest location to you
– Pin to dashboard: Check this option for easy access
to your IoT hub from the dashboard
• Click Create. Your IoT hub might take a few
minutes to create. You can monitor the progress in
the Notifications pane

Register an IoT Edge device

• Create a device identity for your simulated device so that
it can communicate with your IoT hub
• In the Azure portal, navigate to your IoT hub.
• Select IoT Edge (preview) then select Add IoT Edge
Device

Register an IoT
Edge device
• Create a device
identity for your
simulated device so
that it can
communicate with
your IoT hub
• In the Azure portal,
navigate to your IoT
hub.
• Select IoT Edge
(preview) then
select Add IoT Edge
Device

• Give your simulated device a unique device
ID.
• Select Save to add your device.
• Select your new device from the list of
devices.
• Copy the value for Connection string—
primary key and save it. You'll use this
value to configure the IoT Edge runtime in
the next section

Configure the IoT Edge runtime
Install and start the Azure IoT Edge runtime on your device

• The IoT Edge runtime is deployed on all IoT Edge devices
• It comprises two modules
• The IoT Edge agent facilitates deployment and monitoring of
modules on the IoT Edge device
• The IoT Edge hub manages communications between
modules on the IoT Edge device, and between the device and
IoT Hub
• When you configure the runtime on your new device, only the
IoT Edge agent will start at first
• The IoT Edge hub comes later when you deploy a module

• Configure the runtime with your IoT Edge device connection
string from the previous section
– iotedgectl setup --connection-string "{device
connection string}" --auto-cert-gen-force-no-
passwords
• Start the runtime
– iotedgectl start
• Check Docker to see that the IoT Edge agent is running as a
module
– docker ps

Deploy a Module
Manage your Azure IoT Edge device from the cloud to deploy a module which will send telemetry data to IoT Hub

Deploy a Module
Manage your Azure IoT Edge device from the cloud to deploy a module which will send
telemetry data to IoT Hub
• One of the key capabilities of Azure IoT Edge is being
able to deploy modules to your IoT Edge devices from
the cloud
• An IoT Edge module is an executable package
implemented as a container
• In this section, you deploy a module that generates
telemetry for your simulated device
• In the Azure portal, navigate to your IoT hub.
– Go to IoT Edge (preview) and select your IoT Edge device.
– Select Set Modules.
– Select Add IoT Edge Module.
– In the Name field, enter tempSensor.
– In the Image URI field, enter microsoft/azureiotedge-
simulated-temperature-sensor:1.0-preview.
• Leave the other settings unchanged, and select Save
• Back in the Add modules step, select Next.
• In the Specify routes step, select Next.
• In the Review template step, select Submit

Deploy a Module
Manage your Azure IoT Edge device from the cloud to deploy a module which will send telemetry data to IoT Hub
• Return to the device details page and select Refresh.
You should see the new tempSensor module running
along the IoT Edge runtime.

View Generated Data
• In this tutorial, you created a new IoT Edge device and installed the IoT Edge runtime on it
• Then, you used the Azure portal to push an IoT Edge module to run on the device without having
to make changes to the device itself
• In this case, the module that you pushed creates environmental data that you can use
• Open the command prompt on the computer running your simulated device again
• Confirm that the module deployed from the cloud is running on your IoT Edge device
– docker ps
• View the messages being sent from the tempSensor module to the cloud.+
– docker logs –f tempSensor

What Next?
• You can do a lot with what you have done and use the
generated data
• Stream Analytics
– Azure Stream Analytics provides a richly structured query
syntax for data analysis both in the cloud and on IoT Edge
devices
– Azure Stream Analytics (ASA) on IoT Edge empowers
developers to deploy near-real-time analytical intelligence
closer to IoT devices so that they can unlock the full value of
device-generated data
https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-deploy-stream-analytics

What Next?
• Machine Learning
– Analyzing real-time
sentiment on
streaming Twitter
data.
– Analyzing records
of customer chats
with support staff.
– Evaluating
comments on
forums, blogs, and
videos.
– Many other real-
time, predictive
scoring scenarios.
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-integration-tutorial

AWS IoT
• AWS IoT services enable you to easily and securely
connect and manage billions of devices
• You can gather data from, run sophisticated analytics on,
and take actions in real-time on your diverse fleet of IoT
devices from edge to the cloud
• They offer multiple solutions...

AWS IoT

AWS IoT Solutions
Amazon
FreeRTOS
IoT operating system for microcontrollers
OpenSource
AWS
Greengrass
Local compute, messaging, data caching, sync, and ML inference
capabilities for connected devices
AWS IoT
Core
Easily and securely connect devices to the cloud with an IoT platform
Reliably scale to billions of devices and trillions of messages

AWS IoT Solutions
AWS IoT
Device
Management
Onboard, organize, monitor, and remotely manage
connected devices at scale
AWS IoT
Device
Defender
Security management for IoT devices
AWS IoT
Analytics
Run analytics on massive volumes of IoT data without
having to worry about all the cost and complexity typically
required to build your own IoT analytics platform

AWS IoT Solutions
AWS IoT
1-Click
Trigger AWS Lambda functions from simple devices
Compute service that runs your code in response to events and
automatically manages the compute resources (Serverless computing)
AWS IoT
Button
Cloud Programmable Dash Button

Challenges with Cloud and best practices
230
https://cloudacademy.com/blog/disadvantages-of-cloud-computing/

1. Downtime
• Since cloud computing systems are internet-based,
service outages are always an unfortunate possibility and
can occur for any reason
• Can your business afford the impacts of an outage or
slowdown?
• An outage on Amazon Web Services in 2017 cost publicly
traded companies up to $150 million dollars and no
organization is immune, especially when critical business
processes cannot afford to be interrupted

Best Practices for minimizing planned
downtime in a cloud environment
• Design services with high availability and disaster recovery in mind.
Leverage the multi- availability zones provided by cloud vendors in your
infrastructure
• If your services have a low tolerance for failure, consider multi-region
deployments with automated failover to ensure the best business
continuity possible
• Define and implement a disaster recovery plan in line with your business
objectives that provide the lowest possible recovery time (RTO) and
recovery point objectives (RPO)
• Consider implementing dedicated connectivity such as AWS Direct
Connect, Azure ExpressRoute, or Google Cloud’s Dedicated Interconnect
or Partner Interconnect
– These services provide a dedicated network connection between you and the
cloud service point of presence
– This can reduce exposure to the risk of business interruption from the public
internet Dr Ganesh Neelakanta Iyer 232

2. Security and Privacy
• Any discussion involving data must address security and privacy,
especially when it comes to managing sensitive data
• Of course, any cloud service provider is expected to manage and
safeguard the underlying hardware infrastructure of a deployment
– However, your responsibilities lie in the realm of user access
management, and it’s up to you to carefully weigh all the risk scenarios
• Though recent breaches of credit card data and user login
credentials are still fresh in the minds of the public, steps have been
taken to ensure the safety of data
– One such example is the General Data Protection Rule (GDPR), recently
enacted in the European Union to provide users more control over their
data
– Nonetheless, you still need to be aware of your responsibilities and follow
best practices

Best practices for minimizing security and
privacy risks
• Understand the shared responsibility model of your cloud provider.
• Implement security at every level of your deployment.
• Know who is supposed to have access to each resource and service
and limit access to least privilege.
• Make sure your team’s skills are up to the task: Solid security skills
for your cloud teams are one of the best ways to mitigate security
and privacy concerns in the cloud.
• Take a risk-based approach to securing assets used in the cloud
• Extend security to the device.
• Implement multi-factor authentication for all accounts accessing
sensitive data or systems

AWS Shared Responsibility Model
Dr Ganesh Neelakanta Iyer 235https://cloudacademy.com/blog/aws-shared-responsibility-model-security/

3. Vulnerability to Attack
• In cloud computing, every component is online, which
exposes potential vulnerabilities
• Even the best teams suffer severe attacks and security
breaches from time to time
• Since cloud computing is built as a public service, it’s
easy to run before you learn to walk
• After all, no one at a cloud vendor checks your
administration skills before granting you an account: all it
takes to get started is generally a valid credit card

Best practices to help you reduce cloud
attacks
• Make security a core aspect of all IT operations.
• Keep ALL your teams up to date with cloud security best practices.
• Ensure security policies and procedures are regularly checked and reviewed.
• Proactively classify information and apply access control.
• Use cloud services such as AWS Inspector, AWS CloudWatch, AWS CloudTrail, and
AWS Config to automate compliance controls.
• Prevent data exfiltration.
• Integrate prevention and response strategies into security operations.
• Discover rogue projects with audits.
• Remove password access from accounts that do not need to log in to services.
• Review and rotate access keys and access credentials.
• Follow security blogs and announcements to be aware of known attacks.
• Apply security best practices for any open source software that you are using

4. Limited control and flexibility
• To varying degrees (depending on the particular service),
cloud users may find they have less control over the function
and execution of services within a cloud-hosted infrastructure
• A cloud provider’s end-user license agreement (EULA) and
management policies might impose limits on what customers
can do with their deployments
• Customers retain control of their applications, data, and
services, but may not have the same level of control over
their backend infrastructure

Best practices for maintaining control and
flexibility
• Consider using a cloud provider partner to help with implementing,
running, and supporting cloud services
• Understanding your responsibilities and the responsibilities of the cloud
vendor in the shared responsibility model will reduce the chance of
omission or error
• Make time to understand your cloud service provider’s basic level of
support
– Will this service level meet your support requirements?
– Most cloud providers offer additional support tiers over and above the basic
support for an additional cost
• Make sure you understand the service level agreement (SLA) concerning
the infrastructure and services that you’re going to use and how that will
impact your agreements with your customers

5. Vendor Lock-In
• Vendor lock-in is another perceived disadvantage of cloud
computing
• Differences between vendor platforms may create
difficulties in migrating from one cloud platform to another,
which could equate to additional costs and configuration
complexities
• Gaps or compromises made during migration could also
expose your data to additional security and privacy
vulnerabilities

Best practices to decrease dependency
• Design with cloud architecture best practices in mind
– All cloud services provide the opportunity to improve availability and
performance, decouple layers, and reduce performance bottlenecks
– If you have built your services using cloud architecture best practices, you are
less likely to have issues porting from one cloud platform to another.
• Properly understanding what your vendors are selling can help avoid lock-
in challenges
• Employing a multi-cloud strategy is another way to avoid vendor lock-in
– While this may add both development and operational complexity to your
deployments, it doesn’t have to be a deal breaker
– Training can help prepare teams to architect and select best-fit services and
technologies
• Build in flexibility as a matter of strategy when designing applications to
ensure portability now and in the future.

6. Costs
• Adopting cloud solutions on a small scale and for short-term
projects can be perceived as being expensive
• Pay-as-you-go cloud services can provide more flexibility and
lower hardware costs, however, the overall price tag could
end up being higher than you expected
• Until you are sure of what will work best for you, it’s a good
idea to experiment with a variety of offerings
• You might also make use of the cost calculators made
available by providers like Amazon Web Services and Google
Cloud Platform

Best practices to reduce costs
• Try not to over-provision, instead of looking into using
auto-scaling services
• Scale DOWN as well as UP
• Pre-pay if you have a known minimum usage
• Stop your instances when they are not being used
• Create alerts to track cloud spending

Summary
• Going forward, the cloud will allow Industry 4.0 to develop
mature, automated systems that will provide a flexible and
fully connected supply chain where human interaction
reduces to a minimum through autonomous predictions
and self-regulating supply chains
• Cloud platforms are being developed to securely capture
and analyse information from a range of devices to
automate and optimise business processes

Are we all set to loose out jobs?
• “ROBOTS ARE STEALING OUR JOBS”
(Entrepreneur, April 2019)
• “AI EXPERT SAYS AUTOMATION COULD REPLACE 40%
OF JOBS IN 15 YEARS” (Fortune, Jan. 2019)
• “JOB LOSS FROM AI? THERE’S MORE TO FEAR!”
( Forbes, Aug. 2018)

Don’t panic – just prepare

Are you ready for a career you’ve never
even dreamed of?
Children entering primary school today will work in jobs that
don’t exist yet. And the likelihood is, so will you. In fact,
according to the Dell Technologies’ Realize 2030 Report, 85%
of jobs in 2030 haven’t been invented yet. That’s just over 10
years away, and will certainly affect your work life

ni_amrita@cb.amrita.edu
ganesh.vigneswara@gmail.com
GANESHNIYER
http://ganeshniyer.com/
https://www.amrita.edu/faculty/ni-ganesh

Cloud and Industry4.0

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