►
From YouTube: Accelerate AI with the Open Hybrid Cloud
Description
Business leaders desire data driven insights to help improve customer experience. Data engineers, data scientists, and software developers desire a self-service, cloud-like experience to access tools/frameworks, data, and compute resources anywhere to collaborate, build, and scale. This keynote will highlight AI/ML use cases, execution challenges, and tools to help accelerate AI/ML projects from pilot to production, and accelerate delivery of intelligent applications. Finally, the session will share real world success stories across a number of open source projects led by Red Hat, including Open Data Hub.
A
My
name
is
mike
peach,
I'm
vice
president
general
manager
of
the
cloud
storage
and
data
services,
business
unit
at
red
hat-
and
I
am
super
excited
to
be
here
with
you
all
today-
I'm
going
to
talk
a
little
bit
about
how
you
can
accelerate
your
artificial
intelligence
and
machine
learning
projects
and
efforts
and
initiatives
with
open,
hybrid
cloud.
So
let
me
jump
right
in
so
I
always
try
to
situate
a
talk
like
this
within
some
current
context,
and
sometimes
that
can
be
a
little
a
little
difficult,
a
little
a
little.
You
know
contrived.
A
Let's
say
in
this
case
we
actually
have
a
really
fascinating
event:
series
of
events
underway
right
now.
That
is
just
absolutely
rich
with
inspiration
and
starting
points
to
talk
about
data
data,
engineering,
data,
science,
artificial
intelligence,
machine
learning,
so
unless
you're
just
completely
off
grid,
you
can't
not
have
heard
that
a
very
large
ship
was
stuck
in
the
suez
canal
for
a
number
of
days.
Last
week
it
is
called
the
ever
given
stuck
here
in
the
suez
canal
in
egypt.
A
A
A
Let's,
let's
just
look
at
a
couple
of
quick
statistics:
80
percent
of
the
import
export
volume,
the
trade
in
the
world
goes
by
ships
50
by
value
1.5
tons
per
year
per
person
on
the
planet.
A
lot
of
stuff
moves
by
ships,
the
suez
canal,
120
miles
long
owned
by
the
egyptian
government.
50
ships
a
day
go
through
this
again
strategic
location,
9
billion
worth
of
goods
per
day.
13
of
the
world's
trade
goes
through
this
strategic
location.
A
Now
I,
as
I
was
learning
about
this
over
the
last
couple
of
days.
This
particular
image
just
struck
me
for,
among
other
reasons,
the
sheer
difference
in
scale
between
old
technology
and
new
technologies,
often
just
mind-blowing
overwhelming
now.
This
particular
incident
ended
reasonably
well.
Yesterday,
the
ship
was
in
fact
floated
through
a
lot
of
amazing
engineering,
as
well
as
a
little
help
from
from
mother
nature,
with
some
spring
tides
on
the
over
the
weekend
on
sunday,
but
a
very
important
takeaway
here
and
really
this
is
the
setup-
is
that
data
can
help.
A
A
Data
data,
engineering
and
data
science
are
all
critical
now,
if
we
just
think
about
this
whole
scenario,
what
happened
with
this
ship,
its
impact
on
world
trade
on
world
business,
and
we
start
to
think
about
some
of
the
different
kinds
of
data
that
came
into
play.
It's
mind-blowing
right,
so
just
the
the
ship
itself
right.
A
You've
got
fuel
considerations
both
for
the
ships
and
the
fuel
that
the
ships
were
carrying
from
various
places
around
the
world.
You
have
so
many
different
kinds
of
specialists
and
they're
scheduling,
an
availability
specialist
to
pilot
the
ship
specialists
to
dredge
the
ship
out,
specialists
in
re-optimizing
and
rerouting
various
trade
routes,
and
so
on.
You've
got
raw
materials
supply
and
demand
at
a
local
or
sort
of
more
tactical
level.
You've
got
macroeconomic
factors,
black
swan
events,
blah
blah
blah.
A
It
just
becomes
quickly
overwhelming
how
many
different
kinds
of
data
come
into
play
in
situations
like
this,
and
this
doesn't
matter
whether
you're
the
operator
of
the
ship,
whether
you're,
the
seller
of
goods
that
are
being
transported
by
the
ship
you're
the
operator
of
a
port.
There
are
just
so
many
ramifications,
so
many
implications,
so
many
percussions
of
an
event
like
this,
all
of
which
can
be
significantly
improved
and
aided
with
the
right
use
of
data.
A
So
clearly
data
critical
asset
and,
depending
on
what
type
of
industry
you're
in
it
can
be
used
in
different
ways.
It
can
improve
a
customer
experience.
It
can
allow
businesses
to
gain
competitive
advantage.
It
could
be
about
a
p
l,
profit
and
loss
cost
savings.
It
can
be
about
automation,
etc
and
in
different
vertical
domains.
In
different
types
of
business.
A
There
are
clearly
even
more
sub
domains
and
specific
ways
along
the
lines
of
some
of
those
things
I
just
mentioned
where
data
can
be
brought
to
bear
and
machine
learning
more
significantly.
More
recently
can
be
brought
to
bear
in
very
helpful
and
very
important
ways,
whether
it's
in
health
care,
faster,
better
diagnosis,
risk
analysis
and
financial
services,
optimizing
network
routing
within
telecommunications,
how
insurance
premiums
are
calculated,
etc.
A
Now
an
important
consideration
to
keep
in
mind
is
that
operationalizing
the
use
of
data,
the
employment
of
data,
whether
it's
for
really
basic
artificial
intelligence?
Let's
say
simple
rules
based
systems,
whether
it's
more
advanced,
actual
modern,
sophisticated
learning,
algorithms
putting
all
of
this
together
is
not
trivial.
There's
a
lot
of
limelight
there's
a
lot
of
discussion
about
specific
algorithms
and
specific
technologies.
A
A
But
all
of
these
different
types
of
application
building
stakeholders
need
to
be
able
to
collaborate
together
to
get
an
actual
full-blown
application
out
and
in
production,
and
then
ultimately,
you've
got
the
ops
folks
now
whether
you're
running
an
application
in
a
public
cloud
or
or
on
your
own
premises
in
a
data
center,
you've
got
folks
that
need
to
keep
the
lights
on.
You
need
to
keep
everything
up
and
running.
You
need
to
handle
backups
and
restores
and
and
all
of
that
good
stuff.
A
So,
let's
quickly
so
we've
we've
talked
about
a
couple
of
challenges
already,
but
let's
just
highlight
a
couple
of
additional
ones
right.
So
the
data
itself,
as
we
already
touched
on
with
the
shipping
example
there's,
the
volume
variety
and
velocity
of
different
kinds
of
data-
is
of
a
scale
that
is
like
never
before,
and
that
is
overwhelming
old
ways
of
handling
data
right.
So
to
do
modern
machine
learning,
one
needs
different
architectures
from
what
one
needed
in
the
past,
as
with
anything
new
you've
got
a
a
scarcity
of
expertise
to
deal
with
this.
A
Now,
at
the
center
of
all
of
this,
the
next
layer
down
the
platform
layer
that
that
is
critical
for
enabling
the
kinds
of
speed
the
kinds
of
scale,
the
kinds
of
reliability
that
we
need
to
do:
the
kinds
of
application,
development
and
and
running
of
machine
learning,
enhanced
applications
in
production.
This
is
where
a
cloud
platform
this
is
where
containers
and
the
architecture
that
containers
enable
such
as
microservices,
fine
grain,
modularity,
etc.
A
So
now,
with
all
of
that
background,
let's
take
a
look
at
a
couple
of
examples:
three
in
fact,
from
three
different
industry:
verticals,
we'll
start
with
financial
services,
so
royal
bank
of
canada,
top
ten
bank
in
the
world
been
around
for
a
fairly
long
time.
86
000
employees,
lots
of
branches,
a
big
bank.
A
A
They
were
finding
that
projects
took
two
months
to
get
off
the
ground.
The
platforms
were
hard
to
build.
The
the
just
the
sheer
wiring
together
of
the
various
technologies
at
at
the
disposal
of
the
engineers,
was
just
itself
very
time
consuming
and
distracting
and
taking
away
from
the
time
to
actually
build
the
applications
and
security
and
compliance
were
challenges
as
well.
A
A
A
They
their
architecture,
taking
advantage
of
kubernetes
and
the
the
container
architecture
of
openshift
employed
a
very
fine-grained
architecture
to
deploy
machine
learning
models
in
containers
so
that
they
could
get
that
rapid
iterative
structure
to
their
process.
They
were
particularly
because
of
regulatory
constraints,
and
so
on,
wanted
to
set
this
up
any
sort
of
on-premises.
You
know
their
own
data
center.
The
nvidia
technology
was
significant
in
speeding
up
what
was
initially,
you
know.
Some
performance
challenged
applications
there,
so
the
results
they
took.
A
A
So,
let's,
let's
jump
to
a
different
vertical,
a
different
domain
and
with
certainly
different
drivers
and
different
constraints.
Healthcare,
so
hca
healthcare,
private
healthcare
company
based
in
the
u.s
also
been
around
for
50
plus
years.
A
couple
hundred
hospitals,
2
000
care
sites
across
the
u.s
and
as
well
in
the
uk,
50
billion
dollars
in
revenue
number
67
fortune
500.
So
a
big
health
care
organization.
Here
in
the
us,
280
000
employees,
health
care
in
terms
of
employees
per
unit
of
work
or
per
revenue
et
cetera.
A
A
A
The
diagnosis
of
sepsis
was
a
very
manual
process,
literally
nurses,
with
clipboards
and
the
knowledge
about
how
to
diagnose.
It
was
also
spotty.
There
was
better
knowledge
in
some
places
than
others
and
that
needed
to
be
that
needed
to
be
well
addressed,
so
they
again
hca
set
out
to
address
this
very
specific
problem
to
automate
and
normalize
this
diagnosis
across
all
of
their
vast
properties
and
give
every
possible
diagnosis,
instance
the
benefit
of
the
the
best
possible.
A
They
provide
a
five
hour
head
start,
there's
there's
a
great
video.
That's
that's
linked
with
an
interview
that
talks
about
every.
So
every
hour
is
of
delay
in
diagnosing
sepsis
increases.
The
odds
or
the
risk
of
of
death
by
four
to
seven
percent,
so
hours
really
is
the
difference
between
life
and
death
here,
and
this
is
just
a
fantastic
example
of
how,
with
the
right
kind
of
infrastructure,
supporting
data
science,
how
it
can
be
rolled
out
on
a
mass
scale
and
really
really
help
humanity.
A
Okay.
Third
third
use
case
here:
let's
look
at
automotive
manufacturing,
so
bmw
group,
everybody's,
I'm
sure
you
know
heard
of
and
and
seen
bmw's
on
the
road.
Eighth
largest
automaker
in
the
world
been
around.
For
you
know,
over
100
years
they
roll
out
two
and
a
half
million
cars
a
year.
Now,
bmw
has
always
prided
itself
on
its
image
of
innovation
right.
They
made
the
first,
their
first
electric
car
in
1972,
so
as
an
innovative,
technology-minded
technology-oriented
company.
A
A
A
Now,
bmw
wanted
to
manage
so
they
have
a
a
program
called
connected
drive.
So
if
you
happen
to
be
an
owner
of
a
late
model,
bmw
you're,
probably
familiar
with
their
connected
drive
application,
allows
you
to
do
everything
from
you
know:
navigation
to
scheduling,
maintenance
to
you
know
ordering
a
pizza
while
you're
on
the
road.
A
A
The
expectation
that
every
car
out
there
is
is
going
to
be
connected
their
whole
software
development
organization
had
to
be
itself
transformed
so
again,
solution
based
on
openshift.
They,
they
developed
the
d3
data-driven
development
platform
to
to
throw
out
the
massive
amounts
of
data
being
generated
already
by
by
their
cars
out
there.
This
was
all
built
using
the
latest
cloud
native
architecture
of
microservices,
etc.
On
top
of
openshift
and
with
a
a
partnered
development
company
called
dxc,
they
have
really
taken
this.
A
A
You
know
when
you
start
to
think
about
what
gets
short
circuited
by
throwing
machine
learning
at
a
problem
versus
having
humans
figure
it
out.
It's
it's.
It's
pretty
amazing,
okay,
so
we've
walked
through
three
different
example.
Use
cases
of
how
a
hybrid
cloud
infrastructure
has
helped
companies
or
organizations
in
those
three
different
industry,
verticals
significantly
improve
or
augment
their
offerings,
their
customer
experiences
etc.
A
So
all
of
that
has
been
what
I've
gone
through
already
has
all
been
developed
on
openshift
again
that
kubernetes
based
cloud
platform.
So
I
want
to
talk
about
real
quickly
here
in
my
last
couple
of
minutes.
Is
a
project
called
open
data
hub?
So
this
is
an
open
source
project.
If
you
go
to
open
data
hub
dot,
io
you'll
see
what
it's
all
about,
and
in
short,
I
mean
as
said
here,
a
data
and
ai
platform
for
the
hybrid
hybrid
cloud
it
is
built
on
top
of
openshift.
A
Basically,
it
is
an
open
shift
operator
operator,
there's
a
special
construct.
That
is
what
installs
and
sort
of
monitors
the
the
runtime
of
workloads
on
openshift.
So
it
is
an
operator.
It's
a
meta
operator,
if
you
will
that
pulls
together,
different
open
source
projects
that
are
part
of
the
data
science
workflow,
enabling
a
much
easier,
let's
say,
wiring,
together
and
setting
up
of
a
data
science
environment,
to
allow
companies
to
do
the
kinds
of
projects
that
we
just
went
through,
but
much
more
easily
right.
A
A
A
The
workflow
to
deploy
models
as
a
service
on
openshift
is,
is
part
of
this,
either
in
a
simple
way
or
more
advanced
way
with
technologies
such
as
selden
also
incorporated
our
technologies
for
gathering
metrics
and
storing
the
results
of
those
metrics.
So
that's
your
grafana
and
prometheus
technologies
like
that.
So
this
is
what
open
data
hub
is
doing.
A
It's
bringing
together
these
open
source
projects
into
a
coherent,
relatively
seamless
environment,
to
empower
data
scientists,
data
engineers,
machine
learning
engineers,
all
of
the
stakeholders
involved
in
creating
these
intelligent
applications
to
give
them
the
environment
that
they
need.
So
they
can
do
this
rapidly
with
high
performance
at
scale,
without
spending
too
much
time
having
to
do
all
of
the
the
tedious
and
error-prone
wiring
together
themselves.
A
So
let
me
end
with
the
following
a
couple
of
takeaways
that
I
hope
you've
sensed
and,
as
we
touched
on
them
in
some
of
these
case
studies
as
well
as
some
of
the
initial
setup,
the
data
opportunity
will
force
practically
everyone
to
be
hybrid
right.
There
is
the
the
notion
of
a
walled
garden
is
a
fleeting
fantasy
right.
If
anybody
is
imagining
like.
Oh
I'm
just
going
to
go,
buy
a
a
data
science
environment
off
the
shelf
and
set
it
up
and
away.
I
go.
A
A
A
You
want
the
ability
to
pull
in
new
technologies
to
to
to
to
connect
things
in
different
ways,
so,
whereas
in
open
data
hub-
as
I
just
discussed,
some
of
that
is
taken
off
the
table
for
you
so
that
wiring
together
it
shouldn't
be
walled
off
it
shouldn't
be
hidden,
it
shouldn't
be
completely
black
boxed.
You
need
flexibility
and
related
to
that.
There
should
be
a
balance
of
opinionated
constraints
and
freedom
right.
A
So
so,
basically
you
know
there's
that
that
phrase
you
know
you
know,
make
the
make
the
simple
thing
simple
and
make
the
hard
things
possible.
That's
that's
that's
what
you
really
want
to
get
to
here
right.
So
there
is
no
perfectly
you
know
handheld,
you
know,
can't
hurt
yourself
type
of
environment
but
with
the
right
kind
of
opinionation.
The
right
kind
of
you
know.