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From YouTube: AI/ML Powered Healthcare Using Open Source
Description
Training and creating an AI/ML model is only the first, and believe it or not, maybe the easiest step in the model lifecycle. It is only by considering the whole lifecycle of the model (data gathering, deployment, monitoring, retraining,...) that you will be able to put the model to valuable use in production.
In industries, such as healthcare, where speed, reproducibility and security take a prominent role, applying the right architecture patterns, using the right tools, and most of all being able to constantly evolve your solution are the keys to success.
In this session we will:
See how Red Hat pragmatically approaches AI/ML solutions or platforms, along with partners and Open Source projects,
Walk you through different projects and examples we have worked on this past year (such as automated sepsis detection, or a shared data science platform for a Covid-19 research team), allowing AI/ML approaches to be used by healthcare professionals in their daily practice, both for Clinics and Research.
Speaker: Guillaume Moutier
A
Hello:
everyone,
my
name,
is
guillaume
muche,
I'm
from
red
hat
a
little
bit
of
background
just
to
set
the
stage
I
joined
that
two
years
ago.
Prior
to
that,
I
was
the
ceo
of
level
university
in
canada,
I'm
from
canada
by
the
way.
So
that's
what
is
so
hard
for
me
right
now
is
this
weather
and
as
part
of
an
academics
institution,
I
had
the
chance
to
work
with
or
affiliated
hospitals
around
the
city
of
quebec.
A
So
I
know
a
little
bit
of
what
people
and
researchers
are
doing
in
health
care
and
today
we're
going
to
see
how
red
hat
is
helping
them
with
different
use
cases.
So-
and
I
don't
see
anything
here
not
there,
so
we
go
first
through
the
red
hat
and
open
shift
in
data
science.
Just
to
see
how
red
hat
is
helping
with
the
kind
of
tools
that
we
are
providing
and
then
I
will
go
through
different
health
care
projects.
I
had
the
chance
to
work
with
either
as
fully
open
source.
A
A
Okay,
so
think
of
the
linux
kernel
openshift,
which
is
our
kubernetes
distribution
and
tons
of
different
applications
that
come
from
the
open
source
world
and,
of
course,
everything
that
we
do
is
also
for
the
open
source,
even
if
it's
with
a
subscription
which
greatly
enhances
the
chance
that
you
build
successful
data
science
platforms,
because
of
course
you
have
to
interact
with
myriads
of
different
solutions.
That's
where
open
source
definitely
helps
so
perception
versus
reality.
A
A
Training
in
the
rest
is
a
little
bit
smaller
and
we
don't
think,
but
there
is
tremendous
work
to
be
done
on
data
data,
acquisition,
gathering,
cleaning,
preparation
and
the
rest
and
then
putting
all
of
those
together
to
work
in
a
production
environment,
because
at
the
end
of
the
day,
what
we
want
is
something
something
actionable,
especially
in
the
earth
care
industry.
You
know
this
can
save
lives
depending
on
the
accessibility
of
this.
If
we
have
only
models
that
we
can
use
after
six
months
of
working
with
the
data
and
the
rest.
A
Well,
maybe
it's
too
late
for
the
patient.
Okay,
so
that
that
wouldn't
help.
So
what
we
are
trying
to
do
is
to
provide
as
many
tools
as
possible
in
our
environments,
to
help
with
everything
that
is
around
the
iml.
I
try
that
we
don't
train
models.
We
don't
provide
you
with
models,
but
we
provide
you
with
all
the
tools
that
you
would
need
to
create
those
models
and
put
them
into
production.
A
How
we
do
that
we
take
this
machine
learning
life
cycle,
the
standard
life
cycle.
Where
you
have
this
data
preparation,
then
the
training,
then
then
the
deployment,
the
monitoring
and
the
feedback
loop,
where
you
put
back
your
retread
models
on
production.
Of
course,
all
of
this
has
to
run
almost
everywhere.
That's
why
we
have
also
this
openshift
platform,
which
is
our
kubernetes
distribution,
to
be
able
to
run
those
things
almost
anywhere.
So
that's
the
base
infrastructure
and
the
base
architecture
on
which
we
are
building
our
solutions
and
for
data
science.
A
It
was
a
huge
change
for
everyone,
just
speeding
up
the
the
life
cycle
of
your
applications,
so
they
come
with
this
simpler,
lighter
way
of
packaging,
your
applications,
which
is
cool,
it's
still
useful
in
data
science
world,
but
it
brings
also
an
interesting
characteristics,
which
is
the
immutability
when
you
work
in
data
science
and
I'm
not
a
data
scientist
myself,
so
maybe
most
of
the
crowd
here
knows:
where
does
this
better
than
me?
But
the
idea
is
that
whenever
you
do
some
experiments,
you
want
to
be
able
to
reproduce
it.
A
Whenever
you
want
to
do
it
so
meaning
you
can
have
the
same
data,
but
one
year
later,
it
must
run
exactly
in
the
same
conditions.
So
meaning
you
have
to
use
the
same
libraries
you
have
to
use
the
exact
same
code
and
everything
which,
with
the
newest
fancy
stuff
like
tensorflow,
I
buy
torch
and
the
rest
mathematically.
The
way
it
works.
A
You
won't
have
the
exact
same
result,
whether
you
use
tensorflow,
2.7.1
or
2.7.2
or
2.8.
It's
the
same
library.
You
call
it
in
the
same
manner,
but
given
the
same
code
and
the
same
data,
you
don't
have
the
same
result.
That's
where
you
have
to
make
sure
you
package
your
applications
and
your
data
in
some
way
that
it's
totally
reproducible.
You
know
that's
the
base
of
science.
A
Is
this
reproducibility
and
that's
what
containers
are
bringing
so
I
would
say
that
containers
are
totally
adapted
to
the
data
science
world,
to,
of
course,
application
development,
but
even
more
for
pure
data
science.
Because
of
this,
this
reproducibility
that
that
you
bring.
Of
course,
it's
standard
characteristics,
it's
reusable
portable
and
everything.
So
that
means
anything
that
you
have
developed
somewhere
will
be
able
to
run
somewhere
else,
and
that's
where
we
are
going
to
see
in
in
some
examples.
A
So
based
on
that,
we
have
created
open
data
hub
open
data
hub
is
our
solution
at
red
hat
developed
at
the
office
of
the
cto
to
take
the
most
widely
used
data
science
tools
and
data
engineering
tools
and
package
them
in
a
way
that
they
can
run
on
openshift
again
our
community's
distribution,
which
comes
with
some
enterprise
grade
capabilities,
especially
regarding
security
and
the
rest.
So
that
means
you
just
can't
take
any
container
in
any
form
and
just
put
it
on
open
shift
and
it
will
run
now.
A
There
are
some
things
to
do
to
to
to
make
them
properly
run
and
that's
what
we
do
with
jupiter
notebooks
and
kubeflow
argo
triton
from
nvidia
all
this
kind
of
thing
we
make
sure
they
run
and
we
package
it
as
an
operator.
So
that
means-
and
we
can
have
a
demo
for
you
at
the
booth
anytime
in
under
a
few
minutes,
you
can
fully
deploy
data
science
platforms
for
your
data
scientists.
Okay,
just
take
a
few
clicks,
go
from
the
marketplace
and
open
shift,
deploy
the
sense
platform
and
bam
you
have
it.
A
You
can
provide
jupyter
notebooks
as
a
service,
okay,
either
in
the
cloud
on-prem
wherever
wherever
openshift
runs,
which
is
everywhere.
You
can
do
this
kind
of
thing.
So,
based
on
this,
we
created
reddit
open
shift
data
science
because
remember
the
first
project.
Open
data
hub
is
a
full
upstream
project:
community
driven,
that's
not
a
product
that
you
can
buy,
but
red
openshift
data
science
is
the
product
okay.
So
we
take
this
code.
We
repackaged
it
differently
and
openshift
data
science
is
a
managed
service,
meaning
it
runs
fully
in
the
cloud.
A
You
don't
have
anything
to
install
neither
openshift
nor
the
data
science
platform
itself
so
that
it's
an
easy,
easy
way
for
your
data
scientists
to
be
able
to
work.
It
has
two
different
effects.
First,
one,
you
are
sure
that
each
and
every
one
is
using
the
same
tool
at
the
same
version
with
the
same
packages
and
everything
lots
of
times
organizations
they
have
small
data.
Science
teams-
and
everyone
is,
you-
know,
hacking
their
workstation
and
installing
this
or
that
that
they've
seen
you
know,
they've
seen
an
article
on
this,
so
seems
cool.
A
I'm
gonna
do
this
and
then
they
want
to
to
give
it
to
someone
else
in
the
team
and
it
just
doesn't
work
because
the
installation
is
not
exactly
the
same.
The
packages
are
not
exactly
the
same,
and
so
on.
So
here
we
solve
this
situation
by
providing
an
easy
to
use,
serve
service
environment
where
everyone
is
at
the
same
level,
then.
The
second
thing,
of
course,
is
the
security
because,
as
it
fully
runs
inside
this
container
environment,
the
data
never
leaves
this
environment.
A
Okay,
you're
not
at
risk
to
lose
your
laptop
or
whatever,
with
whatever
is
on
it
and
the
other
interesting
thing:
the
increased
capabilities,
meaning
you
cannot
buy
two
gpus
and
256
gigabytes
of
ram
to
each
and
every
data
scientist
in
your
team,
because
sometimes
they
need
to
do
a
heavy
model
training.
No,
that's
just
not
true!
So
here
by
centralizing
those
resources,
you
are
able
to
provide
a
self-service,
on-demand
thing
where
they
can
select.
A
I
want
today
to
work
with
eight
cpus
and
one
gpu,
because
I
know
I
will
do
model
training,
but
then
I
will
release
those
resources
so
that
they
are
available
to
others.
So
it's
it's
a
more
convenient
way
and
secure
the
way
to
to
work
again.
If
you
want
to
demo
about
that,
we
can
meet
in
the
booth.
That's
the
first
release
with
different
components.
So
we
have
the
the
layer.
A
It
runs
right
now
in
the
cloud
on
aws,
soon
coming
to
azure
and
gcp,
and
also
coming
in
a
few
months
on
prem.
So
you
have
your
accelerator
layer
with
nvidia
gpus
that
are
fully
supported
by
nvidia.
You
can
do,
of
course,
gpu
slicing
and
everything
to
share
those
gpus
because
they
are
quite
costly
among
among
people,
and
then
you
have
this
layer
with
the
managed
services
from
that
with
the
the
open
shift,
data
science,
services,
the
notebooks
and
everything
the
model
training
model
serving
part.
A
So
that
means,
if
you
have
terabytes
of
data
to
query,
you
can
definitely
use
this
kind
of
this
kind
of
tools,
pachyderm
for
data
data
management,
data
line
age
and
all
these
kind
of
things,
ibm
watson
and
the
tools
and
libraries
from
intel.
So
if
you
don't
have
any
gpus,
you
still
can
have
accelerated
model
training
and
serving
using
those
libraries.
A
That
was
what
we
do
at
red
hat
and
those
are
some
of
the
dashboards
that
you
will
that
you
will
see.
So
if
you
subscribe
to
the
service
as
a
data
scientist,
you
have
access
to
directly
to
those
environments
where
you
can
select
okay.
Today
I
want
to
work
with
this
flavor
of
jupiter
notebooks,
with
pytorch
or
whatever
you
just
launch
click
and
launch,
and
you
are
in
your
environment.
A
A
A
A
You
know,
statistics
and
everything,
but
each
and
every
day
on
their
website
they
would
publish
the
data
for
the
day,
but
everything
else
was
lost.
So
you
couldn't
look
for
yesterday
and
the
day
before
and
the
week
before,
and
so
on.
So
a
researcher
began,
you
know
to
gather
this
data,
put
up
some
spreadsheets
and
everything
and
share
with
friends
and
so
on.
A
So
a
community
began
to
to
to
be
created
and
they
went
up
to
about
300
people,
so
researchers,
practitioners,
people
from
different
government
agencies
and
the
rest,
and
they
set
up
this
platform
where
they
would
be
able
to
share
their
data,
to
work
together
on
the
data
and
to
publish
this
website
where
people
would
have
tons
of
dashboards
and
analysis
on
kavit
19.
But
it
was
also
a
platform
where
those
people
could
collaborate
and
they
would
publish
the
results
as
in
a
way
that
was
actionable
by
the
people
in
research
or
in
medicine
in
general.
A
So
the
way
we
did
this
is
that
we
used
open
data
hub
deployed
in
the
cloud
in
the
ws
so
that
that's
what
and
we
did
it
for
free
all
this
end,
that's
kind
of
it
was
kind
of
red,
that's
effort
towards
the
pandemic.
We
helped
them
set
up
this
platform
where
they
could
easily
collaborate.
So
that
means
each
researchers.
You
know
they
they
would
be
separated
with
different.
Does
it
work?
A
No,
no,
no
different
projects
and
everything
they
would
have
access
to
the
data
science
tools,
so
jupyter
notebooks
and
argo
to
create
workflows
and
everything
they
would
work
together
on
this
and
then
at
the
end
they
would
publish
their
thing.
The
interesting
part
was
that,
of
course,
there
were.
There
was
some
data
that
was
freely
available.
A
There
was
some
other
data
that
was
more
restricted
and
some
data
that
was
heavily
restricted,
so
we
were
able,
by
separating
the
access
rights
and
everything
to
have
each
and
every
researchers
based
on
their
group
and
the
authentication
and
the
rest
have
access
to
united
data
sets
that
they
needed
only
to
the
notebooks
that
they
that
they
would
want
to
work
on
and
so
on.
So
they
were
able
to
work
now
this
project.
Now
that
pandemic
is
almost
ending.
A
It's
it's
really
scaled
down,
but
it
was
a
good
example
on
how
you
can
bring
many
different
people
from
different
organizations
working
together
in
a
shared
on-demand
data
science,
environment,
and
because
they
were
data,
scientists,
researchers,
mds
and
everything.
They
wouldn't
have
known
how
to
create
this.
Okay.
Originally
they
were
just
exchange,
exchanging
spreadsheets
and
working
through
msn,
messenger
or
stuff,
like
that,
just
no,
it's
total
hell.
So
if
they
wanted
to
be
able
to
scale
down
to
the
300
people,
that's
the
kind
of
platform
that
that
that
they
needed
okay,
another
example
hta
healthcare.
A
A
Okay,
just
look
not
waiting
for
the
sepsis
to
append
to
happen,
but
acting
on
some
data-
and
this
is
you-
know,
correlation
of
many
different,
many
different
data
from
the
patients
and
the
patient
history
and
everything,
but
there
they
are
able
to
do
that.
So
that's
quite
that's!
Quite
cool
we've
worked
a
lot
with
the
veterans
affairs
in
the
u.s,
so
they
are
the
one
taking
care
of
health
care
for
all
the
veterans
in
the
u.s.
If
you
don't
know
this
community,
I
have
a
few
figures
here.
A
So
that's
9.2
million
people,
that's
huge
cohorts.
Any
data
scientists
would
die
to
have
access
to
the
kind
of
data
that
they
have.
That's
totally
crazy
tons
of
practitioners
of
of
medical
centers
and
the
rest.
But
you
see
the
disturbing
thing
here
at
the
bottom
is
the
17
years
it
takes
from
research
translated
to
translated
to
practice
to
practitioning
okay,
so
they
want
to,
of
course,
speed
up
this
way,
speed
up
the
time
from
new
knowledge
being
acquired
to
something
that
is
really
actionable
into
into
their
into
their
environments.
A
So
they
are
working
on
different
projects
and
everything
is
based
exactly
as
what
I
described
on
the
openshift
datasense
platform.
In
their
case,
it's
with
open
data
hub
the
open
source
version,
because
everything
is
heavily
secured
and
on-prem,
but
still
they're
leveraging
openshift
and
this
kind
of
data
sense
platform
for
first
project
which
see
which
is
the
agile
mda
card.
It's
it's
a
model
that
follows
a
patient
all
along
their
journey.
Okay,
just
to
make
sure
that
the
medication
they
are
given
follows
exactly
what
it
shows
because,
of
course,
the
mds
they
cannot.
A
You
know,
have
everything
in
their
head,
and
so
here
we
are
helping
with
this
model
to
make
sure
that
the
treatment
they
are
receiving
is
adapted
to
their
current
medication,
to
the
ones
that
they
had
in
history.
So
it's
based
on
the
historical
patient
data,
it's
based
on
their
day-to-day
data
and
metrics
and
the
rest
so
that
they
make
sure
that
they
receive
the
right
medication
at
the
right
time.
Okay,
it's
like
overseeing
control
method,
about
what's
happening
for
those
patients.
A
Another
project
they
are
working
on
is
the
this
conversion
from
long
phone
calls
to
to
video
consultations,
because
they
they
had
this
idea
that
you
know
they
are
because
the
us
is
huge.
They
are
doing
a
lot
of
consultation
by
phone,
but
they
have
detected
this
trend
that
the
longer
the
phone
call
is.
A
There
is
a
good
chance
that
you
should
definitely
see
someone
okay,
so
here
by
analyzing,
the
conversation
in
the
phone
calls
analyzing
the
length
paired
with
the
the
the
the
the
data
of
the
patients.
They
are
trying
to
improve
this
to
early
detect,
which
patient
should
directly
go
through
a
video
interview
instead
of
calling
and
spending
long
hours
on
the
on
those
phone
calls
that's
what
they
are
trying
to
to
achieve
natural
language
processing.
A
Here
it's
to
reduce
the
administrative
burden
they
have,
they
have
tons
of
affiliated
hospitals
or
clinics
or
whatever
they
are
still
sending
facts.
I
don't
know
if
it's
the
same
in
the
uk,
I
can
tell
you
it's
the
same
in
canada.
My
wife
is
a
researcher
in
cardiology.
Whenever
she
wants
to
do
something.
The
doctor
always
oh,
send
me
a
fax
with
the
data.
That's
just
not
workable
data.
You
know
it's
such
a
pity
that
in
2020s
people
are
still
exchanging
data
like
that
with
faxes.
A
That
doesn't
make
sense
so
here
they
want
to
use
natural
language
processing
to
be
able
to
receive
directly
those
facts
process
them.
Of
course,
if
it's
only
you
know
a
prescription,
that's
that's
pretty
easy,
but
when
the
doctor,
you
know,
writes
a
full
full
summary
of
the
interaction
with
the
past
patient
and
everything,
that's
what
they
want
to
analyze
to
be
able
to
directly
categorize
the
patient,
add
some
metadata
in
the
in
its
files
and
everything.
So
that's
that's
what
they
are
doing
with
nlp
another
project.
A
It's
va,
smart,
that's
an
application
that
they
have
for
for
veterans
here,
it's
much
more
on
taking
information
about
the
patient
and
then
giving
suggestions
nudges
to
change
their
life
habits
and
everything.
So
it
can
be
anything
for
diet
from
diet,
recommendation
to
sports
and
everything.
So
exactly
as
facebook
is
doing
with
your
data
and
then
pushing
you
some
some
ads
or
whatever
here
it's,
it
has
a
more
ethical
goal.
A
A
I
have
another
example,
and
here
because
of
time
I
won't
do
the
full
demo
live,
but
there
is
a
recording
it's
at
the
end.
I
guess
the
slides
will
be
shared,
so
you
will
have
the
links
and
everything.
This
is
a
demo
that
I
created
before
the
pandemic.
I
didn't
know
that
it
would
be
so
popular
afterwards,
but
it's
about
a
data
pipeline
for
x-ray
diagnosis,
okay
for
pneumonia,
because
turning
a
model
to
be
able
to
recognize
a
risk
of
pneumonia
from
chest
x-ray,
that's
pretty
straightforward.
A
Nowadays
there
are
tons
of
libraries
and
frameworks
that
will
that
will
do
it
almost
automatically
for
you.
So
what
I
wanted
to
demonstrate
in
this
demo
is
how
you
can
put
this
into
real
production
at
scale.
Okay.
So
the
idea
is
that
we
have
this
model,
but
we
want
to
accelerate
the
to
accelerate
the
processing
and
va
veterans.
Affair
is
a
good
example
for
that
they
have
every
month
about
one
million
images
to
process.
Okay,
that's
the
kind
of
scale
they
are
operating
at.
A
Of
course
they
won't
put
a
radiologist
or
or
a
doctor
in
front
of
each
and
every
of
these
images.
So
they
have
to
devise
a
new
innovative
way
to
do
that.
So
the
workflow
is
this
one
you
have
at
each
medical
facility.
You
have
a
container
image
with
a
machine
learning
model
that
will
automatically
process
the
incoming
image
and
make
a
risk
risk
inference
of
pneumonia.
A
It
will
classify
all
those
images
between
a
high
risk,
low
risk
of
of
pneumonia
and
also
a
low
confidence
in
the
in
the
in
the
prediction
because
of
course
not
every
well.
All
models
are
not
100
accurate.
That
just
doesn't
work
this
way.
So
sometimes
you
have
images
that
the
model
doesn't
know
what
to
do
with
them.
So
what
I
have
in
the
pipeline,
I
am
anonymizing
the
images.
Of
course
it's
a
demo,
but
you
will
see
on
this
images.
A
There
are
some
personal
informations
which
are
fake
for
the
purpose
of
the
demo,
but
just
to
illustrate
that
you
can
modify
anything
the
metadata
that
are
associated
based
on
the
risk.
So,
in
the
case
where
the
model
is
not
sure
about
the
inference,
it
will
anonymize
those
new
images
and
we'll
send
them
to
a
central
location
where
you
can
retrain
the
model
and
then
send
it
back
to
each
and
every
location
where
it's
used.
A
Okay,
so
that's
the
pipeline,
that's
what
you
have
when
you
want
to
train
the
model
with
openshift
data
science,
so,
as
you
can
see,
I'm
launching
a
specific
notebook
server.
So
as
a
data
scientist,
I'm
working
in
my
notebook
environment
and
I'm
creating
the
model
and
the
code
that
will
that
will
do
this
inference
risk
and
then,
at
the
end
of
the
demo,
you
end
up
with
this,
which
is
full
dashboard,
which
shows
you
in
real
time
the
images
coming
in
being
processed
eventually
being
anonymized
and
as
part
of
the
demo.
A
I
also
showcase
how
you
can
change
the
model
midway.
Let's
say
you
have
a
new
version
of
the
model
model
version,
two
that
you
want
to
deploy.
It's
all.
It
also
showcases
how
easy
it's
done
in
openshift.
You
just
push
your
container
to
your
repository
and
then
it's
automatically
deployed
wherever
wherever
it's
used
without
any
done
time
or
anything.
So
that's
an
interesting
way.
We
have
a
demo
on
this.
If
you
want
to
do
the
workshop
for
yourself
get
in
touch
with
red
hat
people.
A
This
is
something
that
that
we
are
giving
full
workshops
on
how
to
create
those
pipelines
or
if
you
want
to
simply
discuss
this
kind
of
things,
I'm
here
till
tomorrow,
even
friday
morning,
but
and
I'll
be
happy
to
discuss
your
use
cases
and
how
we
can
help
with
that.
I
will
leave
you
with
some
links
on
how
to
have
access
to
all
those
those
informations.
A
So
first
openshift
data
science
itself.
If
you
want
to
try
it,
there
is
a
60
days,
free
trial
for
you,
so
you
can
register
and
you
have
full
access
to
openshift
data
science.
If
you
want
to
try
it
for
yourself
and
then
all
the
case,
studies
that
we
have
for
hca
has
my
flattening
and
the
demo
for
the
x-ray
pipeline
and
I'm
in
time,
and
we
even
have
two
minutes
or
three
four
questions.
B
B
Hi
gabe,
thank
you,
so
barry
liddy,
I'm
uk
lead
for
ai
assurance
with
deloitte.
I
guess
my
question
is
linked
to
giving
my
background
more
regulation.
I
guess
the
proposed
eui
act
suggests
that
healthcare
ai,
is
high
risk
and
should
be
regulated.
I
just
want
to
get
your
view
as
to
whether
you
agree
with
that
and
if
so,
what
should
regulation
be
focused
on.
A
There
are
legislations
that
are
being
passed
right
now
about
the
explainability
of
the
models
and
the
rest.
So
there
are
tons
of
things
that
you
can
do
even
with
this
kind
of
legislation.
You
know
xg
boost
or
or
dragon
boost
or
kind
of
thing.
That's!
Okay!
When
you
are
beginning
to
use
neural
networks,
that's
where
the
gray
part
here
is
so
as
it
is
now.
A
The
goal
is
just
to
speed
up
the
process,
so,
ultimately
a
practitioner
as
it
is
right
now
or
and
as
it
should
be,
if
you
ask
my
opinion
on
this-
should
review
the
final
thing,
but
if
we
can
from
the
start,
you
know
tag
the
the
patients
that
you
should
see
right
now
and
not
in
two
hours
or
you
know,
that's
for
the
that's
what
we
are
looking
at
right
now,
a
way
to
more
easily
process
the
those
kind
of
data.
Okay.
I
have
another
example.
A
If
I
have
two
minutes
children's
hospital
in
toronto,
they
are
working
on
something
to
speed
up
times
at
the
emergency
room,
because
you
know
how
it
goes.
You
come
in.
You
wait
two
hours,
you
see
a
nurse,
you
wait
two
hours
and
ultimately
you
see
a
doctor,
but
what
they
found
is
that
normally
with
given
your
symptoms
or
history
and
everything
there's
a
good
chance
that
any
way
the
doctor,
what
they
will
say
is
that
you
should
have
these
more
exams.
A
So
what
they
have
done
is
they
have
trained
the
model,
given
all
those
data
that,
from
the
start,
predicts
that
okay,
there's
a
95
chance
that
any
way
the
doctor
will
ask
for
those
exams.
So
the
nurse
now
is
abducted
to
directly
ask
for
this
exam
so
that
when
the
doctor
is
there,
everything
is
there
and
he
can
make
the
diagnosis
he
or
she
can
make
the
diagnosis.
So
that's
about
speeding
up
thing,
but
not
replacing.
C
A
Okay,
we
definitely
not
competing
with
sagemaker
or
azure
ml
or
whatever
it's
just
that
our
clients
were
asking
for
some
data
science
solutions
on
openshift,
because
that's
their
platform
to
go
for
containers
and
container
orchestrations
and
the
rest.
Of
course
you
will
find
the
exact
same
tools,
jupiter
notebooks,
byte
or
tensorflow
nimitz,
but
here
it's
our
way
to
port
it
to
openshift
in
a
more
integrated
way
with
openshift.
A
If
you
come
and
see
our
workshops,
for
example,
we
we,
we
guide
you
from
training
a
model
on
this
and
then
directly
deploying
the
model
on
openshift,
okay,
where
sagemaker
will
tell
you,
okay,
the
model
is
trend
now
figure
out
how
to
containerize
and
how
to
deploy
and
how
to
do
the
lifecycle,
because
openshift
was
a
platform
for
devops.
It
totally
suits
for
mlaps
and
the
rest,
because
it's
the
same
tools
and
it's
the
same
way
of
thinking
about
deploying
the
things.
A
So
that's
where
we
differentiate
a
little
bit
plus
that's
the
managed
service
that
we
have
for
now.
That
will
come
on
all
cloud
platforms
and
also
on-prem,
but
right
now
on-prem
people.
They
are
heavily
using
open
data
hub,
which
is
the
upstream
version,
so
it's
not
supported
yet,
but
they're
still
using
it.