La Inteligencia Artificial (IA) está de moda y todas las organizaciones corren para tratar de ser parte de esta revolución. ¡El problema es que la carrera es cuesta arriba! ¿Tienes los datos correctos? ¿Están bien catalogados? ¿Tienes las habilidades profesionales correctas? ¿Tienes los procesos de negocio listos? ¿Puede toda tu organización participar de éstos proyectos, o sólo están reservados para unos pocos privilegiados? En una sólida estrategia de IA hay un componente, acaso el más importante, que habitualmente ignoramos. De hecho, este componente tiene el efecto de aplanar la curva de Tiempo/Valor. En un mundo de asistentes virtuales, redes neuronales, y más... la INFRAESTRUCTURA importa más que nunca. No sólo desde el punto de vista del hardware sino en un sentido más holístico... como un conjunto de marcos teóricos, instrumentación y funcionalidades. En esta presentación vamos a hablar acerca de cómo los proyectos de IA (Inteligencia Artificial) obtienen enormes beneficios al definir una AI (Arquitectura de la Información) que los acompañe.

1. Marcos Quezada
@mRcSqZd

3. weather alerts
10
number of opportunities
to talk to policyholders in
an average year
97%
policyholders
value alerts
for weather
52%
take action to
protect their
property
6.1%
those that
took action &
filed a claim

4. magicnot magic

5. BI and Data
Warehousing
Self-Service
Analytics
New Business
Models
insight-driven transformationmodernizationcost reduction
renovate innovate
most
are here
value
most
are here
insight-driven transformationmodernizationcost reduction
AI
operations business intelligence self-service analytics new business models
renovation innovation

6. BI and Data
Warehousing
Self-Service
Analytics
New Business
Models
insight-driven transformationmodernizationcost reduction
renovate innovate
most
are here
value
we help
you get
here
insight-driven transformationmodernizationcost reduction
renovation
operations analytics self-service analytics new business models
innovation
Cloud Private
Cloud Private for Data
Cyber Resiliency and Modern Data Protection
Big Data
Hybrid
Multicloud
AI

7. first automobile driven
down the streets
of Detroit
in 1896
20 years later
mass production
was streamlined
and cars became
available to the
mass market
components
and integration
had to be built
and improved
upon over time

8. sometimes technology has a long gestation period
before it can be scaled for everyday use

9. Organize
trusted analytics foundation
integrate, cleanse, catalog, govern
Collect
make data simple & accessible
virtualize access – don’t duplicate
ship analytics to the data
scale insights with
AI everywhere
Analyze
operationalize
with trust
Infuse

10. IBM Open Source Based AI Stack
Accelerated
Compute
Intelligent
Storage
Watson Studio
PowerAI Vision
Watson
OpenScale
business KPIs
production metrics
explainability fairness
continuous evolution
Watson
Machine Learning
Watson ML Accelerator
data preparation
data exploration
model development
Watson
Knowledge
Catalog
data profiling
quality & lineage
data governance
ORGANIZE
FOR AI
BUILD
AI
model deployment
model management
model retraining
OPERATE
AI
DEPLOY, RUN &
MANAGE AI

11. WOULD YOU
WATCH MORE?
3X
LESS DIVING?
MORE SCORING?
HADOOP

12. the wisdom
of the cr owd

13. Repair
Estimate $650
Repair
Estimate $2650

14. 34 min of
data analysis
33 min of automated feature
engineering & model buildings
95.7%
1hr 7min
4 hours data
preparation
4 hours feature
engineering
1hr code dev and
data exploration
1hr creating 5 models to
find best solution
95.5%
10 hours
Kaggle Titanic
Competition

15. you could have the fastest servers in the world,
but if you can’t get to the data …
you have the slowest servers in the world

16. WML-A with
Spectrum Conductor
ready to commence
training

17. AI Model FactsBuild Time 1 hour 45 (1000 epochs)
Number of features 56
Determines credit worthiness for loans under $5000
Scores Approved (A) | Denied (D) | Further Review (FV)
Top feature weightings
Late Payments (engineered) 56%
R3 – (90 days) 42%
R2 – (60 days) 32%
R1 – (30 days) 26%
Time at residence (engineered) 17%
* Scored by “Residential Determination” algorithm which
creates blended overall score that penalizes multiple moves
within 3 years. Algorithm ID# E423. Details bitly.housing-algo.com.
*
Current savings 8%
Time at employer (engineered) 13%
* Scored by “Employer Determination” algorithm which creates
blended overall score that penalizes multiple jobs
within 2 years. Algorithm ID# E923. Details bitly.job-algo.com.
*
Current wealth 6%
Chief Scientist Rodney Reid
GDPR Compliance Officer Shannon Pickard
Data is source under guidelines of Doc #12233. FICO credit risk
score not used per E42323 exemption for low income loan
application pool under Rebuild Credit promotion.
first_neuron':[4, 8, 16, 32, 64], 'hidden_layers':[0, 1, 2], 'batch_size': (1,
5, 5), 'dropout': (0, 0.2, 3), 'weight_regulizer':[None], 'losses':
[binary_crossentropy], 'optimizer': [Adam, Nadam, RMSprop],
'activation':[relu, elu], 'last_activation': [sigmoid]
Hyperparameters

18. WATS N anywhere
everywhere