Summary Nuggets

Question 1

AI use cases:

Answer 1

· Correct Typos/Grammar Spelling
· Understand Spoken Commands
· Aisles in Kroger are optimized
Read handwriting

Question 2

Supervised Models

Answer 2

Cat vs dog that were tagged by a human

Question 3

Unsupervised Models

Answer 3

no labels, looks for natural groupings [cluster analysis; associate rule]

Question 4

Reinforcement Learning Models

Answer 4

tradeoff between exploration and exploitation = using the existing model to drive decisions. [multiarmed bandits]

Question 5

Four Principles of Transformation

Answer 5

1. One strategy
   
   2. Architecture clarity
   3. Agile Product focused organization
   4. Clear multidisciplinary governance

Question 6

Starting point for IT Security

Answer 6

1. Know your assets
   
   2. Protect your assets
   3. Detect
2. Recover

Question 7

Speed of Trust Formula

Answer 7

Strategy X Execution X Trust = RESULTS

Question 8

Network Effect

Answer 8

First Fax machine by itself wasn’t very helpful; need a network to communicate to
AirBNB very well clustered and builds upon itself
Uber not as much as its focused on geographical regions

Question 9

Multihoming

Answer 9

ability to be on two networks/across platforms. Alexa wants you on their network and no other. iPhone and android similar [opposite of walled garden]

Question 10

Millionaire Mindset

Answer 10

Integrity is key, having passion for your customer and set your leader standard work

Question 11

People Development –> Drive Line

Answer 11

You should pay enough to take the issue of money off the table –> then the h care about:
1) Autonomy
2) Mastery
3) Purpose

Question 12

Zero Trust

Answer 12

· is a framework, a single product will not solve your problems
· will replace VPN users request and are validated at the application level vs network level
· No more walled garden, microsegment the applications
· Start with highest value/highest risk apps
Use AI/ML to know what normal good traffic looks like

Question 13

Verizon DBIR

Answer 13

(Data Breach Investigation Report)

Question 14

Incident

Answer 14

(1 million in 2022) a security event that is a POTENTIAL EXPOSURE compromises integrity of an asset

Question 15

Breach

Answer 15

(.25 million in 2022) an incident that results in a confirm disclosure of data to an authorized party
83% of all incidents are external actors

Question 16

Actors [security]

Answer 16

who is behind the event?

Question 17

Actions [security]

Answer 17

what tactic or actions did they do?

Question 18

Confidentiality [security

Answer 18

data exposed to an unauthorized actor

Question 19

Integrity [security]

Answer 19

data that is changed from original state

Question 20

Phishing [security]

Answer 20

send dubious attachment

Question 21

Social Engineering [security]

Answer 21

responding to email asking to update banking info (with PRETEXT)

Question 22

Why MFA

Answer 22

MFA is critical, need to do the basic things.

Question 23

CIS Critical Security Controls

Answer 23

more focused on cyber than NIST

Question 24

Vanity Metrics

Answer 24

sound useful but don’t actually measure product performance (app downloads vs orders)

Question 25

If a product isn't continuously used

Answer 25

it's easily replaceable

Question 26

Acquisition

Answer 26

how the users come to your products

Question 27

Activation

Answer 27

users first visit to your product in their experience

Question 28

Retention

Answer 28

the user liked your product to use it again

Question 29

Referral

Answer 29

the user liked the product enough to refer someone

Question 30

Revenue

Answer 30

the user finds the product valuable enough to pay for it

Question 31

Understanding who is out there…5Cs:

Answer 31

* Company- why does the company exists? * Customer-who are their customers? * Collaborators - what external people make this product possible (operators, distributors etc) * Competitions - who is competing for your customers money? Climate - what is the macro environment factors

Question 32

Kano Model

Answer 32

* Basic Feature - what customers expect and will be unhappy if they don't have it (toilet paper in AirBNB, gas pedal in car) * Performance Feature (satisfiers) - elements that are not absolutely necessary but increase customer enjoyment (HAVING SUPER FAST FREE INTERNET AT YOUR HOTEL) Excitement features (delighters) - unexpected wow features that become product differentiators IF YOU ARE MISSING THE CUSTOMER STAY NUETRAL

Question 33

PPI

Answer 33

Pay per impression (PPI) - pay for the add whenever its shown (impression = someone saw the ad)

Question 34

PPC

Answer 34

Pay per click (PPC) - pay when someone clicks on it

Question 35

PPA

Answer 35

Pay per action (PPA) - pay when a final action is complete, aka downloaded the app

Question 36

CTR

Answer 36

Click through rate (CTR) - percentage of people who click through on your add

Question 37

CPI and CPM

Answer 37

Cost per impression (CPI) or cost per 1000 impressions (CPM) - how much you made to have your add shown once

Question 38

CPC

Answer 38

* Cost per click (CPC) - this is the actual price you pay for each click in PPC advertising

Question 39

CLV

Answer 39

Customer lifetime value (CLV)- how much money do you expect to make from this customer over products lifetime

Question 40

CDO Summit - GENAI Takeaways

Answer 40

* We are at a trailblaze moment of GenAI. Need to close the gap in data available to knowledge. * Doing your own GenAI LLM is too cost prohibitive right now, in context learning and passing variables * This isn't an efficiency play, this is a creativity play * ML is hard to explain, GENAI is even harder * 4 safety layers [you can't drive a car without breaks] * Model safety * Data safety * Prompt safety (if the model was trained with sensitive data, a prompt can get it out) * Regulatory safety 70% of all data is unused/untapped

Question 41

Data Mesh vs Data Fabric

Answer 41

* Mesh (decentralized) * Domains own their data as a product * Engineering team focuses on the infra and processes * Fabric (centralized) Publishing and unifying across Meshes

Question 42

Data Literacy

Answer 42

context of the data

Question 43

Getting Value out of Data

Answer 43

* Getting new customers * More value out of existing customers * Operational efficiencies, aka Tech Support * Need to benchmark and show progress and value of data * Self Service and training will uplift your users (RDF training) * If you want AI (and all its value) you need clean data as the solid foundation

Question 44

Steps of ML OPs

Answer 44

1) Explore data 2) Data Prep 3) Model Training and Tuning 4) Model Review and Governance - need to understand lineage of model 5) Model Inference and Serving - Running model in prod and serving up data 6) Model Deployment and Monitoring 7) Automated model retraining

Question 45

Promotion Order

Answer 45

Dev to Staging to Prod Staging has prod data

Question 46

Deploying/Promoting Model or Code

Answer 46

Code will output a model. Pros of code If you promote the code you can auto retrain the model and handle data discrepancies across environments. Pros of model No need to deal expensive retraining or model changing greatly.

Question 47

ML Ops Guiding Principles

Answer 47

1) Automated 2) Secure but self service 3) Repeatability 4) Fast feedback loop 5) Model evaluation 6) Platform and cloud agnostics

Question 48

Steps for a Strong AI Foundation

Answer 48

1) Create strong and scalable data foundation 2) Attack with use cases and tool specific to your industry (architecture and models trained for your industry) 3) Have transparency in the data and the models and the right governance model

Question 49

Jag Nubank Product Podcast Takeaway

Answer 49

Fundamentally different vs incremental Customer obsession. Hire smart people Empowered cross functional teams with critical metrics. Churn and Sean Ellis Test. How would you feel if you could no longer use this product. The responses are: 1. Very disappointed 2. Somewhat disappointed 3. Not disappointed The goal is to reach a stage where at least 40% of your users would answer “Very disappointed” if they could no longer use your product. Mock press release What are the key principles for decisions? How do we put the rules on paper? This will help with empowerment

Question 50

Radical Candor

Answer 50

Care Personally and Challenge Directly

Question 51

Gen AI Financial Sector Use Cases

Answer 51

Will supplement other ML activities (not replace) 1) Dev Enablement 2) Business Intelligent (ask questions about your data) 3) Summarize Feedback 4) Know your customer 5) Chat Bots 6) Hyper personalized marketing material (auto gen)