►
Description
If you are like me, the first time you heard of 'Managed Services' you scratched your head and said 'What are Managed Services? What do they have to do with Model Delivery? Where do I go for Managed Services? And of course, my favorite question with any new technology, are they easy to use?
Join me for a session in which we will learn what managed services are and how they are being more frequently used in hybrid cloud platforms to enhance and help with the Model Delivery process. We will finish the session, by taking a quick look at what managed services are available in the new Red Hat OpenShift Data Science platform.
Let's help you understand what Managed Services are so that you can 'manage' your way into delivering your models faster and more easily!
A
A
In
understanding
what
the
deal
is
about
managed
services
and
model
delivery,
we'll
be
looking
at
the
following
agenda,
we'll
be
looking
at
what
are
managed
services.
Who
cares
about
them?
Where
do
I
find
managed
services
and
what
do
manage
services
have
to
do
with
model
delivery,
in
that
instance,
we'll
actually
be
taking
a
look
at
an
aiml
use
case,
an
actual
project
that
one
of
my
colleagues
is
working
on
and
then
finally,
we'll
hope
to
answer
the
question
are
managed
services
easy
to
use
so
managed
services.
A
If
we
took
a
look
at
it
in
the
generic
I.t
perspective,
are
the
practice
of
outsourcing
the
responsibility
for
maintaining
and
anticipating
the
need
for
a
range
of
processes
and
functions
in
order
to
improve
operations
and
to
cut
expenses,
and
I
decided
to
start
with
the
generic
it
service,
it
managed
services.
Look
in
order
to
get
you
familiar
with
everyday
examples
of
managed
services
that
you
would
find
in
a
normal
it
organization,
and
hopefully
that
would
go
ahead
and
wet
your
appetite
to
think
about
what
managed
services
would
be
good
for
a
data
scientist.
A
A
A
A
Obviously
we
don't
want
to
be
caught
in
a
situation
where
we
could
lose
some
of
our
information
on
our
servers.
If
those
were
somehow
damaged,
and
hopefully
we'll
have
a
rollover
somewhere
where
our
important
information
to
our
company
is
saved,
and
then
we
have
managed
backups,
definitely
for
anybody
who's
working
with
a
lot
of
data,
we
definitely
want
to
do
back.
A
Do
backups
in
order
to
save
our
data
resources,
and
there
are
many
many
many
more
managed
services
that
I
didn't
even
list
that
I'm
sure
that
you
can
think
of,
but
that
doesn't
really
matter
I've
just
given
you
a
general
overview
or
flavor
of
what
managed
services
there
are
now,
let's
flip
to
the
data
science
side.
Now
today,
in
data
science,
we
have
an
added
complexity,
we're
just
not
only
on
on-prem
we're,
adding
the
cloud
as
part
of
the
platform
that
we
work
with
our
hybrid
cloud
environment.
A
Now,
as
we
think
about
managed
services
for
data
scientists,
whatever
services
we
create,
we
have
to
allow
the
data
scientists
to
focus
on
building
their
models,
because,
while
they're
building
their
solutions,
they
want
to
experiment
with
the
latest
bells
and
whistles.
So
our
managed
services
have
to
be
very
flexible
in
order
for
them
to
work
with
open
source
products.
A
A
A
Next,
we
would
look
at
running
experiments
and
creating
models.
We
can
provide
a
jupiter
notebook
environment
for
data
scientists
to
go
ahead
and
model
their
experiments,
and
we
know
that
the
data
scientists
would
like
to
have
access
to
curated
data
science
packages.
Therefore
we
do
we
could
have
things
such
as
anaconda
commercial
in
commercial
edition
integrated
for
those
data,
scientists
that
are
looking
to
take
advantage
of
automl.
A
A
Then
we
have
monitor
the
models
and
to
track
performance
with
this.
In
order
to
monitor
that
model,
performance
and
gleam
insights
from
the
analytics
and
monitor
things
like
the
drift
of
a
particular
model,
you
can
continue
to
use
a
service
such
as
seldom
deploy
or
watson,
machine
learning
and
watson,
open
scale
for
model
monitoring
and
performance
tracking.
Just
to
know
when
you
need
to
retrain
and
redeploy
when
we
look
at
the
overall
picture,
keep
in
mind
that
for
any
it
ops
person,
this
flexibility
can
be
a
bit
of
a
nightmare.
A
It's
not
only
data
scientists,
but
also
the
data
engineers,
those
people
that
are
going
to
curate
or
transform
the
data
and
you've
already
heard
me
talk
about
it.
Ops
really
do
care
about
these
services,
because
the
jobs
that
they
do
actually
these
managed
services
will
help
them
do
their
jobs
quicker.
A
This
is
something
that
I
have
seen
and
as
myself
as
a
data
scientist
have
done.
When
I'm
experimenting
with
a
model,
there
may
be
some
sort
of
resource
out
there
in
terms
of
a
managed
service
package.
That'll
actually
cut
down
the
time
that
I
have
to
code
and
will
allow
me
to
concentrate
more
on
my
model
performance
in
terms
of
trying
to
build
something
to
get
my
model
out
the
door.
A
A
A
A
A
So,
on
top
of
this
infrastructure
and
having
the
ability
to
do
this,
compute
acceleration,
we
should
have
kind
of
a
little
area
where
we
could
pick
and
choose
which
type
of
managed
services
we
would
want
to
work
with,
and
these
are
open
source
managed
services
that
I've
listed
here.
Things
like
starburst
galaxy,
as
I
mentioned
before,
for
data
creation,
ibm
watson
and
jupiter,
where
we
could
go
ahead
and
experiment
experiment
with
building
our
models
and
looking
at
some
of
the
results.
A
So
we've
kind
of
built
our
layers
again,
we
have
the
infrastructure,
we
have
compute
acceleration.
So
now,
let's
go
back
to
those
groupings
or
headings
of
our
managed
services
and
see
what
we
could
pick
in
each
one
and
see
what
other
details
they
have
in
them.
So
we've
talked
about
gathering
and
preparing
the
data
that
also
takes
into
account
anything
like
data
storage.
Where
are
we
going
to
store
our
data?
Is
it
going
to
be
an
s3?
A
A
A
A
When
the
model
is
deployed,
we
have
to
continuously
monitor
it
and
manage
it,
and
we
really
want
to
make
sure
that
those
models
are
not
only
making
the
right
predictions
but
that
they
slowly
aren't
drifting
over
time,
so
that
we
don't
notice
that
they're
making
incorrect
predictions
and,
of
course,
at
the
very
end,
we
have
to
have
the
ability
to
retrain
the
models
as
needed.
We
don't
want
to
have
to
be
able
to
pull
everything
down
and
start
from
scratch.
There
has
to
be
a
graceful
way
that
we
can
actually
retrain
those
models.
A
The
machine
learning
model
that
he's
developed
is
actually
going
to
detect
the
license
plate
on
a
vehicle,
and
if
the
vehicle
is
angled
like
this
one
here,
it
actually
will
write
the
license
plate
and
then
the
characters
would
be
gathered
through
ml
data
can
then
be
stored.
Read
analyzed
in
this
case
through
kafka
and
kafka
is
an
open
source
software
which
provides
a
framework
for
storing
reading
and
analyzing
streaming
data.
A
A
So
this
is
confession
time
for
me.
The
architecture
that
we
put
together
for
the
managed
services
that
a
data
scientist
could
use
to
deploy
a
model
actually
exists
right.
Now,
it's
in
a
product
that
we
call
red,
hat,
openshift
data
science
and
it's
a
managed
services
offering
that
sits
on
top
of
openshift,
specifically
openshift
dedicated
and
that
openshift
dedicated
instance
sits
on
top
of
amazon
web
services.
So
that
way,
as
a
user
you
kind
of
have
the
best
of
both
worlds.
A
A
And
this
is
an
example
that,
once
the
users
have
enabled
the
components
in
this
case,
they've
enabled
jupiter
hub
and
what'll
be
available,
for
them
are
all
these
quick
starts
and
and
tutorials
and
documentation
for
for
common
common
tasks,
and
one
thing
I'll
mention
too
is
once
a
user
goes
ahead
and
chooses
a
managed
service
doesn't
mean
that
they
cannot
go
backwards
and
choose
not
be
able
to
choose
anymore.
They
absolutely
can
that's
part
of
the
discovery
and
exploration
when
you're
creating
an
aiml
model.
A
A
Therefore
we're
going
to
begin
by
using
jupiter
hub
so
that
we
can
quickly
experiment
with
the
data
and
again
note
just
because
we
use
jupiter
hub
managed
services,
it
doesn't
mean
that
we
can't
integrate
with
other
other
services,
and
you
can
use
as
many
managed
services
as
you
like.
Realistically,
other
parts
of
the
system
will
fill
in
gaps
that
you
may
not
have
built
out
in
your
in
your
workflow.
A
In
this
specific
step,
here,
you're
going
to
be
able
to
customize
your
notebook
image,
type
deployment,
size
and
environment
variables
and
remember
that,
when
we're
creating
this,
this
image
for
this
container,
the
the
container
for
those
who
may
not
be
familiar
with
it,
consists
of
an
entire
runtime
environment
for
an
application
plus
all
the
dependencies.
So
any
of
the
libraries
binaries
configuration
files
that
are
needed,
all
those
items
are
included,
and
then
it's
bundled
into
one
package
and
by
containerizing
this
application
platform
as
dependencies
differences
in
os
distribution
underlying
infrastructure.
A
A
A
A
If
you
are
working
like
I
do
sometimes
in
aws
and
you
have
data
stored
in
an
s3
bucket,
you
want
to
use
a
jupiter
notebook
instead
of
going
ahead
and
keying
in
the
access
key
id
and
the
secret
access
key
right
into
your
jupiter
notebook
where
the
world
could
actually
see
it.
There's
the
ability
right
here
to
use
environment
variables
and
set
them
up
with
those
secret
passwords
so
that
when
you
go
into
your
jupiter
notebook
nobody's
going
to
see
those
items.
A
Okay,
so
then
we
have
this
server
go
ahead
and
start
up.
This
can
take
a
few
minutes
for
an
image
to
to
spin
up.
You
can
click
on
the
event
log
right
here
so
that
you
can
see
what's
happening
in
the
image
deployment
process,
but
because
this
is
just
a
can
demo
we're
just
going
to
go
to
the
next
step.
A
We
will
enter
in
the
repository
name
where
we
have
some
of
the
work.
That's
been
done
for
this
license
plate
recognition,
software
so
we'll
just
enter
in
the
name
of
the
repository
and
then
press
clone
and
the
license
plate
workshops
will
appear
then
under
the
the
name
column
and
they
will
show
up
in
in
their
own
repository
directory.
A
So
when
we
learned
how
to
create
that
code
that
we'll
be
able
to
extract
the
number
from
a
given
license
plate,
but
of
course
you
can't
really
go
use
a
jupiter
note
like
notebook
like
this
in
a
production
environment.
I
have
known
people
that
have
tried
to
do
that.
It's
not
a
good
idea
reach
out
to
me.
If
you
want
to
find
out
more
about
that,
but
yeah.
Please
don't
do
that.
A
Therefore,
instead
of
using
the
jupiter
notebook,
what
we
did
is
we
packaged
all
the
code
that
you
saw
previously
as
an
api
that
can
be
directly
queried
from
another
application,
and
we
do
this
by
creating
a
flask
app,
but
first
some
explanations:
the
code
that
we
wrote
in
that
notebook
has
been
repackaged
as
a
single
python
file.
So
basically
we're
calling
it
prediction.pi
and
basically
it's
just
the
code
that
was
in
the
cells
of
the
notebook
that
was
put
together
in
that
single
file
and
then
to
use
that
code
as
a
function.
A
So
in
this
case,
we're
going
to
go
ahead
and
launch
the
service
or
launch
the
server,
we'll
go
ahead
and
test
out
our
flask
app,
and
we
can
do
this
by
just
using
a
curl
and
then
we
can
wait
for
the
results.
So
in
this
case,
when
we
did
a
curl
and
we
got
a
prediction
of
s7
jdb
for
the
license
plate
on
the
car
that
we
were
previously
looking
at
and
now
that
the
code,
at
least
from
what
we
can
see,
appears
to
be
working.
A
We're
ready
to
package
it
as
a
container
image
and
have
it
run
directly
into
openshift
as
a
service
that
you
can
call
from
any
other
application.
Remember,
we
could
have
used
something
like
selden
to
deploy
our
application
as
well,
we're
just
using
a
source
to
image
just
so
that
we
can
show
you
what
that
looks
like.
So,
let's
go
ahead
and
build
the
application
inside
of
openshift
dedicated.
A
That
means
we're
going
to
step
away
from
this
red
hat,
open
shift
platform
with
the
managed
services
for
a
second
and
then
we're
going
to
go
into
our
main
openshift
dedicated
platform,
and
this
is
not
a
far
jump.
It's
just
one
click
to
get
back
into
openshift
dedicated.
It's
not
like.
You
have
to
start
up
another
session
and
then
go
search
for
the
other
session
that
you're
in.
I
know
that
that's
something
that
makes
data
scientists
fairly
irritated
when
you're
in
the
middle
of
a
model,
and
you
want
to
test
something
out.
A
As
I
mentioned,
this
is
the
main
openshift
dedicated
platform
and
within
openshift.
We
want
to
make
sure
that
we
have
a
project
or
a
name
space
set
up
for
us
to
work
in,
and
we
do
have
one
just
a
user
one
project
very
bland
and
then
what
we'll
do
is
import
our
license
plate
code
from
our
get
repository,
so
we
had
cloned
a
get
repository
and
as
we
made
changes,
we
could
then
push
those
changes
back
up
to
get
so
that
when
we
get
back
into
openshift
dedicated,
we
can
import
code
from
our
get
repo.
A
A
So
when
we
have
an
application
listening
at
that
route,
that
was
creating
by
deployment,
we
just
test
it
by
just
clicking
on
that
route,
link
or
just
going
ahead
and
doing
a
copy
of
that
url
and
pasting
it
in
the
browser-
and
in
this
case
the
status
was
okay,
so
everything
seems
to
be
working
and
just
a
note.
You
can
also
test
your
app
status
through
curl
or
invoke
a
web
request,
as
our
application
is
now
a
rest
api
endpoint.
There
are
multiple
ways
to
to
upload
images
to
it.
A
If
we
look
at
what
red
hat
has
for
the
typical
aiml
work
flow
life
cycle,
it's
going
to
resemble
very
closely
what
we
kind
of
built
on
our
own
with
managed
services
and
a
platform
to
to
to
support
that,
and
again
remember
for
managed
services.
We're
looking
at
gathering
and
preparing
data
being
able
to
use
something
like
starburst
galaxy,
to
help
us
go
ahead
and
curate
that
we
could
also
use
openshift
streams
for
apache
kafka.
A
A
A
So,
in
a
nutshell,
I
hope
that
you've
gone
ahead
on
this
journey
with
me
and
learned
what
managed
services
are
you
define
those
four
and
kind
of
broke
them
down
into
smaller
ones?
Seeing
who
cares
about
them?
It's
not
only
the
data
scientists,
but
it
they
are
the
people
that
work
with
the
data
scientists
such
as
the
data
engineers,
could
be
some
developers
and
I.t
devops.
A
They
have
to
have
a
say
in
those
services
as
well.
You
can
find
those
managed
services
open
through
open
source.
In
our
case,
with
red
hat,
we
created
a
platform
where
you
can
access
those
managed
services
to
to
create
your
your
actual
model
and
work
with
the
model
delivery,
and
those
managed
services
in
our
case
were
through
a
public
cloud
on
aws,
but
you
could
also
find
those
managed
services
on
a
hybrid
cloud
or
on-prem
and
really,
at
the
end
of
the
day,
what
those
managed
services
have
to
do
with
model
delivery.