Event around IBM Artificial Intelligence solutions hosted by InTTrust.

1. A Hitchhiker’s Guide to AI
The IBM Highway
Michail Pagiatakis
Cloud & AI Solutions BDM
InTTrust S.A

2. AGENDA
AI
Business
Unusual42
Infra
Does it
Matter?
Automatic
for the
People
IBM
A Powerful
Ally
Pride &
Prejudice

3. 42
The answer to life the universe and everything

4. Self driving cars react to
changing conditions
Waze provides a
personalized driving
experience
Uber delivers food that
you like and is the right
temperature
Netflix provides
personalized
recommendations
AI is Everywhere
Influencing Everything We Do
Some Very successfully Some not as successful
But ALL based on DATA and AI
1. Business Value

5. Chilean forestry companies (two examples) Timber harvesting $20M/year + 30% fewer trucks
UPS Air network design $40M/year + 10% fewer planes
South African National Defence Forces Force and equipment planning $1.1B/year
Motorola Procurement management $100M–150M/year
Samsung Electronics Semiconductor manufacturing 50% reduction in cycle times
SNCF (French rail network) Scheduling and pricing
$16M/year rev + 2% lower operating
expense
Continental Airlines Crew rescheduling $40M/year
AT&T Network recovery 35% reduction spare capacity
Grantham, Mayo, Van Otterloo & Co. (GMO) Portfolio optimization $4M/year
Source: Edelman Finalists, http://www.informs.org or http://www.scienceofbetter.org
Delivering massive return on investment
5

6. A transportation
company used analytics
to optimize route
planning every 10
minutes
The company saved millions of
dollars annually by eliminating
miles of unnecessary driving.
Manage hundreds of
constraints on tankers,
drivers and cargos to
increase profits and ensure
safety
With an IBM ILOG CPLEX
Optimization Studio solution,
the company found the
optimal combination of
drivers, trailers and tank-
washes across its whole
logistics network.
C H A L L E N G E : S O L U T I O N :
6

7. FAKE
mix and
match your
algorithms
built on-
premises
with those in
the cloud

9. PowerAI Vision: ”Point-and-Click” AI for images & video
Label Image or
Video Data
Auto-Train AI Model
(no coding, just point and click)
Package & Deploy
AI Model

10. PowerAI Vision core capabilities

11. Upload video and select Label Objects
Pick which frames you want to
label objects in or let PowerAI
Vision automatically capture
frames on an interval you specify

12. Label the frames with object names
Using a subset of the video
frames, label the objects with
bounding boxes exactly as
you would for regular image
object detection

13. Build a simple model and then use it to auto-label more frames

14. My original tagged frames
Auto labelling helps to label more frames
I just need to add in other labels it missed
and then retrain with this additional data
for improved accuracy

15. Test your model – upload a video and click “Detect”

17. that in
turn requires
much higher
computation
to train
as neural networks go
deeper, they provide
a dramatic increase
in accuracy.

18. requires different sets
of skills
FINE-TUNE & DEPLOY
experience all
that pain again
and iterate
MAINTAIN
ACCURACY
iterate
faster and
do it againassisted
parameter
selection and
tuning
~80% of an AI project’s
is time spent here
DATA
PREPARATION
up and
running
over a
quick
lunch time spent
drops from
80% to 30%
extremely long
training times
curtailing broader
proliferation
BUILD, TRAIN,
OPTIMIZE
9 days
to train a
model
becomes
4 hours
weeks to months
UP & RUNNING imagine if everyday
users could contribute
business domain expertise,
help with data preparation,
and even build initial
models so data science
teams could focus on fine
tuning the models
data science skill needed as
the hard stuff happens here
- help with quick detection
of sub-optimal hyper
parameter selection, ‘what
if’ exploration ...
this is where you want data
science teams to
spend their time

19. hardware software
Watson Machine Learning Accelerator
co-optimization + open innovation

20. data
preparation
score
model
deploy
model
build, train, and
optimize model
ingest new data,
re-score and
deploy new model
up
and
running
$$$
monetize
infer
model
maintain
model
accuracy

21. 21
“likely to be one of
the most attractive
platforms in the future,
modern, open,
flexible, and suitable
for a range of users,
from expert data
scientists to
business users.”
2017 reddot
Design Award
Winner
IBM Watson Studio Local

22. 22
Case for AI Automation: AI Workflow’s Bigger & More Complex
Majority spent on
data wrangling!
Ground Truth
Gathering
Data Cleansing
Feature
Engineering
Model Selection
Parameter
Optimization
Ensemble
Model
Validation
Model
Deployment
Runtime
Monitoring
Model
Improvement
Source: https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
AI Lifecycle
Management

23. IBM’s Strategy for Automation of AI Development
Transfer Learning
• Transfer knowledge learning in
one deep learning system to
apply to a different domain
• Featured in Watson Services,
available through Watson Studio
Neural Network Search
• Just bring data and
automatically generate a custom
deep neural network through
searching the best architectures
for the input data
• NeuNetS as a feature of Watson
Studio
AutoAI Experiments |
Pipeline optimization
• Auto clean data, engineer
features, and complete HPO
to find the optimal end to end
pipeline
New

24. Watson OpenScale along withWatson Studio andWML enables
enterprises to operationalize AI across the enterprise
24
IBM TOOLS
Data Scientist App Developer
Build AI RunAI
3RD PARTY IDE &
FRAMEWORKS
IBM AI RUNTIME
Watson OpenScale
Automated Anomaly and Drift
detection
Business KPIs
Watson Studio Watson Machine Learning
Manage AI at scale
3RD PARTY RUNTIMES
Build Deploy and run Operate trusted AI
Business user
Consume AI
Fairness and Explainability
Inputs for Continuous Evlolution
Accuracy
Validation and Feedback
SPSS Modeler
Custom (Kubernetes etc.)
Microsoft Azure ML
Amazon Web Services
Keras
Pytorch
Scikit-learn
Spark ML
Caffe2 …
Watson Knowledge
Catalog
Data Profiling
Quality and Lineage
Data Governance
Organize and
Govern data
Data Engineer
Organize Data for AI

25. IBM Watson OpenScale
Automate & Operate AI at Scale
Production monitoring for compliance
Detect and mitigate model bias; audit and explain model
decisions
Ensure models resiliency in changing
situations
Detect drift in data and anomaly in model behavior;
specify inputs and triggers to model lifecycle
Align model performance with business
outcomes
Correlate model metrics and business KPIs to measure
business impact; actionable metrics and alerts

26. IBM Watson & Cloud © 2019 IBM Corporation *** IBM & Business Partner Only *** 26
Business stakeholders do not trust AI.
60%
of companies see regulatory constraints as a
barrier to implementing AI.
– IBM IBV AI 2018
63%
cite availability of technical skills as a
challenge to implementation.
- IBM IBV AI 2018
Without expensive Data Science resources handholding multiple
AI models in a production application:
1. No way to validate if AI models are compliant with
regulations and will achieve expected business outcomes
before deploying
2. Difficult to track and measure indicators of business
success in production
3. Resource intensive and unreliable processes for ongoing
business monitoring and compliance
4. Impossible for business users to feedback subtle domain
knowledge into model lifecycle

27. IBM Watson & Cloud © 2019 IBM Corporation *** IBM & Business Partner Only *** 27
Why worry about AI and compliance?
A leading bank was fined $175M for charging
higher fees and interest rate to more than 30,000 minority
borrowers.
A leading credit card company was fined $75M
for selling products to Spanish speaking customers at a
much higher interest rate compared to others.
http://www.fsb.org/wp-
content/uploads/140407.pdf“… 50%of our bonuses are linked to how we manage risk …”
– VP, AI platforms, Large American bank

28. Align model performance with business outcomes
Correlate model metrics and business KPIs to measure business impact
Actionable metrics and alerts
Ensure that models are resilient to changing situations
Detect drift in data and anomaly in model behavior
Specific inputs and triggers to model lifecycle
Production monitoring for compliance and safeguards
Detect and mitigate model biases
Audit and Explain model decisions
Model Validation and acceptance
Watson OpenScale will help validate and monitor AI models, deployed anywhere, to
help comply with regulations and mitigate business risk
Foundational to all AI
implementations
Required in regulated industries and
use cases – FSS, HR etc. in short
term; others longer term*
Required to meet
transformational goals
* E.g. Fair lending practices in finance vs. GDPR across all industries
Current capability
Upcoming capability

29. Deploy enterprise AI anywhere,
on the cloud(s) of your choice
Unify AI across private, hybrid,
and multi-cloud landscapes
Align AI services and workflows
with the data they rely on
Tap into the open innovation of
Cloud Paks and OpenShift
29
Watson Anywhere
One Platform, Any Cloud
Cloud-native container platform and
operational services
Cloud Pak for Data
A one of a kind, pre-integrated set of data and AI services
delivered within an open and extensive cloud native platform
Hyperconverged
Private Cloud System
Organize Data Analyze DataCollect Data Infuse AI
Everything you need for enterprise AI

30. 30
Watson
Studio
Watson
Machine
Learning
Watson
OpenScale
Watson
Knowledge
Catalog
Data Profiling & Prep
Quality & Lineage
Policy-based Governance
Visual Design
Develop & Train
Lifecycle Mgmt
Run & Optimize
Model-ops
Dynamic Retraining
KPIs & Accuracy
Explainability& Lineage
AutomatedOptimization
Prepare and
Organize Data
Build and Train
AI Models
Deploy and Run
AI Models
Manage and Operate
Trusted AI
One Unified Experience
Watson AI Tool Suite
A modular set of tools for creating and operationalizing custom AI models
AutoAI Lifecycle Automation – “AI generating AI”

31. Watson APIs
• Speech and language
• Tone and visual recognition
• Empathy and personality
• Behavioral Insights
• Conversational
Build interactive AI applications, processes and
product experiences accessible from any device
Swift
Watson Studio
Supported by an active Github expert community

32. Watson Applications
Speed time-to-value with pre-built AI applications for common use cases
Watson
Assistant
Watson
Discovery
32
Business
Automation
Financial
Crimes
Business
Analytics
Case Management
Robotic Automation
Anti-money Laundering
Payments & InsurancePlanning & Budgeting
Business Intelligence
Sales Forecasting Customer Cross-sell Conduct Surveillance
Strategic Partnerships
Watson
Health
Compare & ComplyExpert Assistance
Voice of the Customer
Next-gen Call Center Knowledge Work
Customer Experience
Payer / Provider
Care & Benefits
Patient Experience
* * *