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Description
Artificial intelligence (AI) is a key component in the successful digital transformation of financial institutions. Deriving insights from data through machine learning plays a key role in accelerating business and mission critical initiatives, such as predicting risk, fighting financial crime or improving customer interaction.
To get the most from their investment in machine learning, AI leaders must focus on the entire machine learning workflow, and enable frictionless interaction between collaborating groups: data scientists, developers, operators. They must also monitor, observe and continuously adapt their models and meet challenges such as high reliability, regulatory compliance, and security.
In this session, we will share Red Hat’s experience of helping financial institutions build intelligent applications and discuss strategies to help you maximize the work you’ve done to deploy AI ML:
- Adopting end-to-end workflows that seamlessly apply machine learning as a business value multiplier in areas such customer experience, fighting financial crime, or risk assessment
- Applying cloud-native technologies to accelerate the implementation of machine learning processes and reduce operational overhead
- Adopting a hybrid cloud strategy for reliability and scalability, while meeting security and regulatory compliance requirements
- Jump-starting analytics work by combining cloud-scale deployments with the freedom of on-demand, scalable and secure analytics environments
Learn more at openshift.com
A
Hello
and
welcome
to
the
virtual
ai
assignment
new
york.
My
name
is
maris
bogovici,
I'm
a
chief
solutions
architect
in
the
financial
services
team,
at
red
hat
and
in
this
session
I
will
share
red
hat's
experience
of
helping
financial
institutions
build
applications
and,
in
particular,
discuss
several
strategies
to
help
you
maximize
the
return
on
your
investment
in
the
in
artificial
intelligence
and
machine
learning.
A
It
is
important
to
understand
the
driver
for
these
changes,
because
financial
institutions
have
been
early
adopters
or
ai,
and
it's
not
a
new
topic
for
them.
Existing
data
have
been
used
for
a
long
time
to
improve
existing
products
into
device.
Algorithms
that
power
specific
bank
functions,
especially
on
the
back
end,
especially
in
the
capital,
markets,
area
or
risk
assessment,
or
even
fraud.
A
Aspects
like
just
in
time
landing
automated
compliance
and
reporting
improved
loan
default
prediction
just
make
the
bank
operations
faster,
quicker
better
and
they
they
help.
You
know
self
their
customers
with
the
same
products,
but
working
so
much
better.
At
the
other
end
of
the
spectrum.
There's
experimentation,
financial
institutions
create
new
business
models
based
on
intelligent
decision
making
and
insights
derived
from
data,
personalized
incentives
and
rewards
client,
advisories
personalized
insights
for
investment.
So
this
way
they
create
they.
A
A
All
this
is
supported
by
a
number
of
emerging
technological
trends
that
help
support
implementing
these
ai
solutions.
Commodity
hardware
provides,
you
know
simpler
and
more
cost
efficient
ways
to
get
the
compute
power
that
you
need.
You
don't
need
a
larger,
bigger
computer.
Then
you
can
get
a
bunch
of
smaller,
cheaper
ones.
A
That
starts,
that
is
largely
unutilized
and
open
source
offers
companies,
freedom
to
innovate
and,
to
you
know
tap
into
the
power
of
communities
to
create
new
and
better
tools
to
implement
in
analytics
and
machine
learning,
workflows
and
in
fact,
the
majority
of
these
new
tools.
The
majority
of
the
of
the
emerging
software
in
the
machine
learning
space.
A
They
must
be
deployed
at
scale
as
we
as,
as
I
mentioned
earlier,
these
solutions
tend
to
become
ubiquitous
so
once
they're
deployed
in
environments.
That,
for
example,
are
part
of
interaction
channels
like
web
and
mobile,
and
deal
with
an
increased
number
of
users.
They
also
need
to
be
able
to
scale
with
increased
usage,
and
you
know
when
these
functions,
for
example,
become
supporting
functions
for
the
entire
enterprise
again
they're
facing
this.
A
A
Financial
institutions
also
must
meet
specific
standards
and
obligations
which
are
kind
of
the
fourth
driving
factor.
When
we
discuss
these
solutions,
they
must
have
the
they
must
be.
Solutions
must
be
secure
and
it
is
important
to
notice
that
machine
learning
models
and
training
data
become
part
of
the
attack
surface
of
the
solution.
Compromising
a
fraud
detection
model,
for
example,
can
create
a
great
loss
at
the
bank,
especially
if
it
goes
undetected.
A
Reliability
is
important
being
able
to
to
run
these
large-scale
workloads
is
a
critical
part
of
actually
building
machine
learning
models,
but
also
once
they're
deployed.
These
models
must
be
highly
available
as
they
become
critical.
Business
functions
and
deploying
aversions
to
production
must
be
performed
with
minimal
downtime,
ideally
no
downtime.
A
Finally,
aml
provides
solutions
to
complex
regulatory
requirement
requirements
and
are
subject
to
regulatory
scrutiny,
so
they
must
comply
with.
They
must
meet
certain
security
criteria
after
we've
seen,
but
also
complied
with
data
access
and
privacy
regulations
and
meet
the
transparency
criteria
set
by
regulators.
A
What
we'll
talk
about
next
is
how
how
red
hat
season,
how
how
resident's
experience
of
helping
come,
help
helping
financial
institutions
deal
with
these
four
driving
forces
and
implement
platforms
that
allow
them
to
take
advantage
of
the
business
opportunities
while
leveraging
technical
advances
in
an
environment.
That
means
the
viability
criteria
and
also
is
compatible
with
with
the
with
the
regulatory
requirements
in
which
they
have
to
function.
A
You
know
the
ai
leaders
and
their
teams.
Must
you
know
clear,
a
number
of
hurdles,
the
you
know,
the
the
the
first
one
is
data
access
you
know
finding
and
preparing
quality
data
is
difficult,
especially
when
regulatory
constraints
restrict
data
access.
So
this
is
something
that
you
know:
companies
have
to
consider
a
workaround,
but
also
finding
talent
and
using
efficiently
that
talent,
especially
when
teams
do
not
collaborate
well
with
each
other
is
another
problem.
A
A
The
usually
there
is
a
much
larger
a
model
is
part
of
a
much
larger
distributed
solution
that
that
incorporates
a
number
of
supporting
activities
and
tasks,
and
this
can
be
easily
seen
in
this
diagram
that
you
know
that
illustrates
I'm
a
solution
for
for
building
an
anti-money
laundering
architecture.
It
is
red
hats
architecture
for
for
for
building
for
anti-money
laundering.
A
That
illustrates
how
artificial
intelligence
and
machine
learning
can
be,
combined
with
other
functionalities
in
the
in
a
bank
in
a
modular
event-driven
fashion
that
leverages
data
from
a
variety
of
data
sources
to
produce
to
build
and
a
cohesive
holistic
solution,
as
opposed
to,
let's
say,
a
monolithic,
end-to-end,
one-size-fits-all
type
of
approach.
But
what's
important
here
is
that
the
while
the
well
the
anti-money
laundering
that
the
machine
learning
capabilities
are
a
critical
part
of
supporting
functionality
such
as
know
your
customer
or
transaction
monitoring.
A
Data
engineers,
data
scientists
and
application
developers,
data
engineers
essentially
are-
are
more
focused
on
collecting
the
data,
transforming
and
preparing
it
for
data
scientists
who
analyze
extract
the
features,
build
models
and
monitor
them,
and
then
application
developers
are
using
these
models
to
build
intelligent
applications
to
build
the
the
business
solution
around
them.
You
know
interfaces
apis
management
tools,
so
all
these
teams
must
work
with
each
other
seamlessly
and
what
we
see
in
typical
machine
learning
workflows,
especially
when
it
comes
down
to
getting
the
work
of
data
scientists
up
to
production.
A
This
is
a
classic
workflow
for
for
performing
machine
learning
tasks.
We
typically
see
two
big
bottlenecks
in
this
process.
One
deals
with
building
discovery
environments
simply
getting
simply
given
data
giving
given
giving
data
scientists
access
to
tools
to
the
tools
that
they
need
to.
The
libraries
that
they
need
is
without
the
need
to
to
file
id
tickets
is
usually
a
problem,
so
moving
from
the
mindset
of
everyone
does
their
work
on
their
own
laptop
and
then
somehow
shares
their
results
to
a
mindset
where
they
have
a
platform
through
which
they
collaborate.
A
It's
a
big
is
a
big
change
here
and
the
other
one
of
course
me
is
concerned
with
moving
these
models
into
production.
Just
kind
of
throwing
notebooks
over
the
wall
is
not
is
not
a
recipe
for
success,
so
integrated
build
pipelines
that
take
the
the
results
of
of
the
earlier
group
and
translate
it
into
seamlessly
working
services.
I
think,
is
the
important
part
and
again
these
you
know
these
kind
of
bottlenecks.
The
solutions
here
share
a
number
of
of
of
common
traits.
A
A
You
know
in
the
case,
for
example,
the
production
deployments,
there's
also
requirements
to
monitor
against
degradation
and
drift.
So
what's
the
solution
for
addressing
these,
these
pain
points
right,
how's
the?
How
do
we
see
this
happening
and
how
how
financial
institutions
have
been
successfully
implementing
such
workflows?
Essentially,
building
a
single
workflow
that
unites
the
multiple
personas
recognizes
the
needs,
their
different
needs
and
provides
self-service
access
to
tools
and
data
and
minimizes
the
burden
of
the
it
operations.
A
A
Essentially,
at
the
top
of
this
architecture,
you
see
the
project
life
cycle,
where
the
business
goals
like
going
from
the
setting
of
the
business
goals
to
developing
the
machine
learning
model
to
implementing
applications
and
inference
and
monitoring
and
retraining
models
as
needed
in
order
to
execute
this
process.
In
order
to
support
it,
you
need
a
machine
learning
software
tool
chain
that
consists
of
you
know,
specialized
libraries
tools
like
tensorflow
and
spark
and
jupyter
notebooks
python.
A
You
know
selden
a
few
other,
and
you
can
recognize
that
you
know
all
of
them
are
are
essentially
open
source
tools,
so
this
is
kind
of
coming
back
to
the
conversation
on
on
the
power
of
open
source.
As
a
as
a
catalyst
for
these
types
of
environments,
so
how
do
you
get
access
to
them
and
use
it
in
a
seamless
fashion?
Is?
Is
the
key
question
now
also?
A
So
all
that
all
of
them
there
is
important
like
having
a
single
platform
on
these,
on
which
these
two,
these
components
can
run
side
by
side
is
critical,
so
the
platform
here
is
is
essentially
the
the
key
component
that
enables
the
seamless
operation
of
these
data
pipelines
and
tools
is.
The
hybrid
cloud
platform
is
a
hybrid
cloud
platform,
with
salt
service
capabilities
based
on
containers
kubernetes
and
with
with
devops
track
with
embedded
devops
practices.
A
As
I
mentioned
earlier,
one
of
the
foundational
blocks
of
this
hybrid
cloud
platforms
is
containers
that
provide
the
agility
flexibility,
portability
and
scalability
for
data
scientists
to
build
and
develop
models
and
to
for
developers
to
code
software
applications
powered
by
these
models.
So,
of
course,
the
you
know
deploying
and
creating
deploying
new
applications
and
getting
access
to
resources
requires
them
to
be
agile
and
not
waiting,
for
example,
for
for
a
ticket
to
I.t
in
order
to
to
deploy
the
task.
A
You
know
the
ability
to
provision
environments
as
needed
and
when
needed
and
finally
being
able
to
aspects
like
scalability
and
self-healing
and
high
availability
means
that
you
know
this
kubernetes
provides
this
natively
for
for
containerized
applications.
So,
and
you
know
you
can
you
can
easily
scale
your
applications
in
a
declarative
fashion
as
managed
by
kubernetes,
so
this
is
essentially
captured
in
the
in
red
hats:
kubernetes
powered
red
hat
openshift
container
platform.
A
That
is
the
foundational
tool
that
helps
accelerating
the
aml
life
cycle.
So
openshift,
of
course,
you
know,
provides
simplifies
the
develop
deployment,
scaling
and
life
cycle
management
of
containerized
eml
tools
right
and
that
like
h2oi,
starburst
presto
selden
and
ensures
high
availability
and
faster
time
to
value
and
also
provides
access,
as
I
mentioned
earlier,
you
know
to
hardware
accelerators,
like
nvidia
gpus,
so
it
means
that
it
acts
as
it
acts
as
the
as
the
glue
between
this
specialized
software
and
the
underlying
capabilities.
A
Finally,
it
is
a
hybrid
it's,
a
fully
integrated
highway
cloud
platform
that
includes
key
capabilities
like
monitoring,
automation,
devops
dual
chain
completely
built
on
open
source
projects.
This
helps
drive
innovation
without
vendor,
lock
in
now
to
illustrate
this
and
to
illustrate
how
this
comes
into
how
this
happens
in
practice,
I'm
using
a
case
study
based
on
you
know
a
successful
implementation
of
such
an
evolving
ai
platform
at
the
royal
bank
of
canada.
A
The
royal
bank
of
canada
is
a
is
a
top
global
bank.
It
is
a
you
know
it
is.
It
handles
massive
amounts
of
data
easily
10
billion
new
clients
interactions
per
month.
It
is
it
has
identified
artificial
intelligence
and
and
using
machine
learning
as
a
critical
business
capability
going
so
far
as
to
create
its
own
research
center
for
for
for
aiml
called
borealis
ai.
A
The
challenge
that
the
royal
bank
of
bannon
had
was
that
aml
projects
do
up
to
two
months
to
get
off
the
ground,
so
they
couldn't
really
see
the
result
of
their
investment
because
the
getting
them
into
production
took
too
long.
So
you
know
there
were
a
number
of
of
symptoms.
Of
that
you
know.
Workflow
workload,
orchestration
and
self-service
access
to
gpu
was
slow.
You
know
it
was
hard
to
build
platforms.
A
You
know
the
actual
applications
and
this,
of
course,
security
and
compliance
requirements
very
hard
to
me.
The
goal
in
the
end
was
to
build
an
ai
platform
that
could
serve
its.
You
know
more
than
a
100
ai
developers
and
engineers
help
them
complete
projects
faster,
but,
most
importantly,
adopt
a
devops
culture
and
to
leverage
cloud
to
improve
efficiency
and
applications.
Release
cycles,
and
the
solution
essentially
was
to
use
red
hat
openshift
to
build
this
platform
in
collaboration
with
nvidia.
A
The
results
are,
you
know,
speak
for
themselves.
More
than
a
thousand
models
have
been
deployed,
experiments
run
10
times
as
fast
models
take
days
to
build.
Instead
of
months
and
the
performance
of
the
system
is,
is
great,
it's
it
can
process
30
million
records
in
less
than
20
minutes.
So
this
not
only
kind
of
helped
rbc
to
meet
its
its
goals,
but
it
also
set
rbc
on
a
path
that
where
it's
able
to
when
it's
thinking
to
leverage
ai
in
customer-facing
applications
as
well.
A
In
summary,
what
we
talked
about
here
today
is
about
how
financial
institutions
that
you
know
have
been
have
a
long-standing
experience
of
using
data-driven
processes
are
launching
ua
initiatives
and
in
order
to
be
successful
with
these
initiatives,
in
order
to
leverage
like
to
meet
the
business
opportunities
to
to
take
advantage
of
the
technological
advantage
and
to
meet
the
business
objectives
and
and
regulatory
obligations,
they
need
to
build,
build
a
platform.
This
allows
self-service
access
to
tools
and
data
and
reduces
the
operational
id
burden.