Fundamentals of Cloud, AI, ML, Blockchain and microservices for product managers

1. This presentation is strictly only meant for educational purposes, not for commercial or business purposes. All pictures and trademarks are owned
by their respective owners.

5. • Powers the modern apps at
scale being price sensitive
• What are the major cloud
components?
• compute
• network
• storage(hot/cold)
• power/data centers
• Security

6. • Cloud fundamentals
• Elasticity
• Security
• Availability
• API model
• Multi tenancy

7. Basics
• Hides the complexity and
details of the underlying
infrastructure from users
and applications
• Provides very simple
graphical interface or API
(Applications Programming
Interface).

8. Basics
• Provides on demand
services, that are always on,
anywhere, anytime and any
place.
• Pay for use and as needed,
elastic
• Scale up and down in
capacity and functionalities
• Services include hardware
and software

9. Software as a Service
(SaaS)
• SaaS is a model of software
deployment where an application
is hosted as a service
• Reduces the burden of software
maintenance/support, less
control
• Other areas
• Platform as a Service (PaaS)
and
• Infrastructure as a Service
(IaaS)

10. Virtualization
• Implement on Virtual Machines (VMs):
• Abstraction of a physical host machine,
• Hypervisor intercepts and emulates instructions
• Provide infrastructure API:
• Plug-ins to hardware/support structures
Hardware
Operating
System
Application Application Application
Hypervisor
Linux Windows

11. Cloud Storage
• Started with data storage capacity that was
hired out to others.
• Amazon’s Simple Storage Solution (S3) is a
well known example
• Unlimited storage*
• PAYG

12. Utility Computing
12
Azure VM/Amazon EC2
• Elastic, marshal 1 to x+
• Auto scale
• Fairly cheap
Linux, Windows, Ubuntu
Management Console/API

13. Challenges
• Complexity of heterogeneous systems
• Security
• Policy and access
• Compliance (GDPR)
• Scale

14. What is Data
Science?

15. What is Data Science?
Study of where information
comes from, what it represents
and how it can be transformed.
1
With the data boom, this field has
taken off primarily because there
is sufficient data, computational
techniques, compute power, etc.
to make it its own field.
2

16. Statistics
Collection, analysis, interpretation and presentation of data
Descriptive
Inferential
Analyst projection of
stock price?
India:
Population: 1.4 Billion
Median age: 23 years
Predictive
Weather Forecast
for tomm?

17. Correlation
Relation- Relationship between two sets of data
Positive: when value is increasing together
Negative: when one value is increasing, the other is
decreasing
HEIGHT SHOE SIZE
109 3
121 5
138 6
155 8
160 9
180 12
191 14

18. Data Model
Information that exists in a variety of formats and sizes
Name: Jack Sparrow
Age: 23
Parents: Adam Sparrow
City: St Kitts, Caribean
Phone: PIRATES-C
Twitter: @pirates
Hobby: Being on ships
Fav movie: Pirates of the
caribean

19. Data Analytics
← If you like this
you will also like
this →
Process of examining data to draw conclusions about that information.
Pirates of the
Caribbean
Another movie of the
same genre, actor or
something related.
Exercise to build a
recommendation
system.

21. What is Artificial intelligence?
Artificial Intelligence-
Mimicking human
intelligence by the
machines.
Fairly generic, all tasks
including NLP, recognizing
objects, cognitive abilities,
translations, autonomy
etc

22. NLP example

23. Computer
vision
Feature matching
Face Detection/People Detection

25. What is Machine Learning?

26. What is Machine Learning?
Machine learning is type of AI, allows
software to be more accurate without
explicit programming

27. Five Questions ML Can
Answer
• Is this A or B?
• Is this weird?
• How much – or – How many?
• How is this organized?
• What should I do next?

28. Five Questions
ML Can Answer
• Is this A or B? (Uses
Classification Algorithms)
• Is this weird? (Uses anomaly
detection algorithms)
• How much – or – How many?
(Uses regressions algorithms)
• How is this organized? (Uses
clustering algorithms)
• What should I do next? (Uses
reinforcement learning
algorithms)
Great simplified model- Microsoft

29. What is Machine
Learning?
• Typically, there are 3 categories:
• Supervised
Learning(classification,
regression)
• Unsupervised
Learning(clustering,
reduction)
• Reinforcement
Learning(reward
maximization)
Lots of generic algorithms can be covered by the umbrella term
“Machine Learning”

30. What about deep learning?
Picture courtesy- Nvidia page on deep learning

31. Pattern detection studies in the real world
- a.k.a. How companies learn about you
• Target was doing a study on shopping patterns
• Here’s what happened:
https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-
out-a-teen-girl-was-pregnant-before-her-father-did/
• Uh-oh!
• Walmart found a correlation between beers and diapers:
https://www.theregister.co.uk/2006/08/15/beer_diapers/
• Moving those two products next to each other increased sales!

33. Bitcoin
• Bitcoin is a cryptocurrency, a
form of electronic cash.
• Invented in 2008
Blockchain
• Public ledger of all transactions
since inception
• Only add, cannot edit or remove

34. Characteristics of Blockchain
1) Unalterable
2) No central ownership
3) Distributed database
4) Consensus algorithm
5) Open to public
6) Private vs public
7) Major players

35. Trust on the
Internet
Purely based on peer to peer
transaction model, removing the
need for a middle person acting
as a trust party

36. Blockchain is
as
foundational
as the
Internet
Allows for the creation of notarized
unalterable “fingerprints” of files
Programmable contracts (smart contracts)
that can interact with Blockchains, or even
run on them (eg: Ethereum),
Allows you to combine record keeping and
asset transfer in one application

37. Blockchain
use cases
Financial transactions
Government dealings,
property notarization
Healthcare

38. Microservices
• How to start?
• Why is it important?
• How companies moved to a Micro Services model?
• What is important to know as a PM while designing?

39. Characteristics of monolithic architectures
Large Codebase
Many
Components, no
clear ownership
Long deployment
cycles

40. Pros
• Single codebase
• Easy to develop/debug/deploy
• Good IDE support
• Easy to scale horizontally (but
can only scale in an “un-
differentiated” manner)
• A Central Ops team can
efficiently handle

41. Challenges
with
monolithic
software
• Difficult to scale
• Operational nightmare
• New release takes months
• Long time to add features
• Frustrated customers
• Lack of agility and innovation

42. Microservices
SOA/loosely coupled
elements that have
bounded contexts

43. Characteristics
• Do one thing and do it well
• Many smaller (fine grained), clearly scoped services
• Clear ownership for each service
• Typically need/adopt the “DevOps” model

44. Core of microservices
• Services communicate with each other over the network.
• Components can be serviced independent of each other.
• Self contained, you can update code without knowing internals of
the other microservice

46. Not about …
● Team size
● Lines of code
● Number of API/EndPoints
MicroService
MegaService

47. MicroServices – separate single purpose services

48. Challenges
Can lead to chaos if not designed right …

49. Right tool for the job
1) Hide your data
2) Be backward compatible
3) Polygot framework- right tool for the job

51. Recommendations
• For AI/ML, please follow Andrew Ng and do his courses.

52. Recommendations
• For being an amazing PM,
please follow these brilliant
people
• https://www.productschool.co
m/product-people/