►
Description
Data science projects can fail before ever being realized. Integrating data science into your application development workflow and tooling from the beginning can greatly improve your chances of success. In this demo, we show how a data scientist uses Red Hat OpenShift Data Science, along with partners and open source software, to develop a model and integrate it as part of a heterogeneous application.
Sophie Watson is a data scientist at Red Hat, where she helps customers to solve business problems using machine learning in the hybrid cloud. She has previously conducted research in the areas of researched Bayesian Statistics and Recommendation Engines and is focused on using her data science and statistics skills to inform next-generation infrastructure for intelligent application development.
Chris Chase is a developer at Red Hat, where he develops next-generation applications in the hybrid cloud. He is an expert in application development and has experience in supporting data scientists deploying their models in production applications.
A
A
Good
morning,
good
afternoon,
good
evening,
wherever
you're
hailing
from
welcome
to
the
first
ever
data
services
office
hour
featuring
data
scientists
holy
smokes.
What's
going
on
smart
people
early
in
the
morning,
so
I'd
like
to
welcome
two
new
faces
to
this
channel.
Sophie
watson
and
chris
chase
sophie's
favorite
algorithm
is
linear
regression
and
chris
chase
is
in
love
with
dogs
apparently
and
has
treats
for
his
doggo
in
his
desk.
I
I
I've,
never
thought
of
doing
that,
but
I
might
in
the
near
future.
B
C
A
In
so
oh
no,
she
laid
down
in
the
doorway,
never
mind,
sorry.
So
for
folks
that
don't
know
my
dog
is
a
german
shepherd.
Corgi
mix
and
people
always
want
to
see
it
when
I
say
that
so
yeah,
so
we
we
were
talking
before
the
show
and
one
of
the
topics
we
were
covering
was
you
know,
there's
a
lot
of
ai
there's
a
lot
of
ml.
There's
all
kinds
of
you
know
things
happening
out
there,
but
those
models
are
hard
to
get
into
applications
or
actually
running
in
production.
A
A
B
B
We
just
want
to
go
off
and
train
that
machine
learning
model
explore
that
data
and
do
what
we're
comfortable
with
and
although
there's
programming
involved
in
that
it
was
certainly
not
you
know,
application
developer
level
of
programming
mastery,
so
I
think
in
general,
data
scientists
work
in
a
way
which
is
not
conducive
to
getting
that
machine
learning
component
into
a
larger,
intelligent
application,
and
so
that
usually
means
I
just
do
my
bit.
I'm
like
yeah
I've.
I've
got
a
machine
learning
program.
C
C
Time
I
go
in
there
and
I
try
and
get
it
working
like
you
know,
like
sometimes
their
cells
don't
run
in
order.
So
they've
gotten
like.
Oh
you,
oh
you
didn't.
You
ran
six
and
then
you
go
up
to
four
and
then
you
go
grade
it
again
and
it'll
load
properly
and
then
usually
for
the
app.
All
I
want
is
like
the
prediction
like
because
I'm
not
going
to
train
in
my
app
like
that's
a
long
async
process
right.
C
Know
if
if
this
thing
is,
if
this
5
is
a
5
or
if
you
know
this
linear
regression,
you
know
what
it's
going
to
spit
out
what
this
prediction
is.
So
I
just
want
that
prediction
and
I
just
want-
I
just
want
its
dependencies.
I
don't
need
all
this
exploration
and
you
know
training
and
all
this
other
stuff.
I
just
need
to
find
that
like
one
function
in
this
dependencies,
but
that's
not
what
I
get.
I
have
to
tease
that
out
and
it's.
C
A
Virtual
environment,
no
nothing!
What
am
I
doing,
yeah
like
so
like
for
the
data
scientists
that
are
out
there.
I
mean
sophie
you've
kind
of-
I
think
maybe
learned
this.
You
know
working
here
just
through
your
normal.
You
know
career
that
you
can't
just
hand
over
an
entire
environment
to
an
I.t
team,
or
you
know
somebody
that's
running
stuff
in
production
and
be
like
here.
You
go
like
there's
a
process
that
we
have
to
follow
to
get
it
into
prod
and
downloading
the
internet's
not
part
of
it.
Usually.
B
Yeah,
I
think
so
I
think
that's
true,
I
don't
want
you,
know
data
scientists
to
look
at
this
and
think.
Oh
gosh,
I've
got
to
change
everything
about
how
I
work
and
I'm
the
problem
here
because
that's
you
know
it's
just
a
completely
different
skill
set
and
a
different
appreciation
for
what
it
takes
to
put
something
into
a
larger
application,
and
I
think,
at
the
end
of
the
day,
as
data
scientists
we
want
to.
We
want
to
find
the
insights
in
the
data
and
we
want
to
share
that
with
people.
B
We
want
to
be
as
useful
as
possible.
So
the
way
that
we've
been
kind
of
approaching
it
here,
a
red
hat-
is
to
try
and
you
know,
teach
some
devops
best
practices
to
data
scientists
so
still
allowing
them
to
work
in
jupiter
notebooks,
not
assuming
that
we're
trying
to
turn
data
scientists
into
application
developers,
but
just
trying
to
you
know,
educate
on
what
it
takes
to
put
something
into
production.
A
Right
and
that's
the
thing
where,
like
it,
teams
and
your
devops
or
sres
or
you
know
all
the
people
and
functional
parts
of
your
pipeline,
getting
applications
to
production,
come
in
and
say,
hi,
let's
work
together
on
this,
so
that
we
can
make
something
usable
for
all
right.
So
teamwork
makes
the
dream
work
in
this
sense
like
for
sure,
like
sorry
to
be
cliche
about
it,
but
you
know
it's
in
my
experience.
A
Working
with
you
know,
data
teams,
it
is
all
about
like
hey,
I
can
build
you
this
thing
to
help
you
make
this
more
portable,
just
run
it
like
this.
You
know
and
you
can
put
anything
in
it.
You
want
and
it'll
be
fine
and
giving
them
the
context
to
do
that.
Helps
tremendously
as
well.
As
you
know,
any
guiding
points
and
kind
of
being
their
help
desk
for
lack
of
a
better
term
like
that
is
definitely
necessary.
C
B
Yeah,
if
we're
up
for
it,
I
mean
we
can
show
you
what
this
kind
of
process
of
getting
machine
learning
into
an
application
is
looking
like
at
the
moment
on
openshift.
So
we
can
use
red
hat
openshift
data
science
for
the
data
scientists,
environment,
so
you'll
be
able
to
see
that
that's
kind
of
it's.
What
a
data
scientist
likes.
It's
got
the
tools,
data
scientists
likes
it's
the
environment,
they'd
like
to
work
in
and
then
I
think
chris
is
going
to
impose
some
structure
on
my
work.
C
C
There
are
a
bunch
of
really
complex
methods
of
getting
data
science
to
work,
and
you
know
we
can
come
cover
that
I
think
in
the
future,
with
like
ci
pipelines-
and
you
know
you
know-
servers
like
selden
and
kf
serving
and
stuff,
but
really
we
could
just
the
two
of
us
could
still
get
our
work
done
just
by
using
openshift
and
like
jupiter
notebooks
and
the
openshift
data
science
platform.
So
here
you
can
see
this
little
template
which
would
make
that
easy
for
a
data
scientist
like
so
to
get
started.
C
It's
got
some
notebooks
here
for
her
to
get
started
and
then,
when
she's
done
there,
she
can
just
take
out
her
prediction:
function,
put
it
in
a
python
file
and
take
out
the
necessary
requirements
for
her
her
prediction
function
and
put
it
into
the
requirements
of
text,
and
if
she
does
that
for
me
like,
I
can
take
this.
This
already
builds
because
it's
already
got
an
application
file
in
it,
really
a
very
small
application
file,
so
we
could
just
like
use.
This
template
create
a
new
project
out
of
it.
C
Right
and
then
like
she's,
ready
to
go
I'll,
give
this
git
repo
to
her
and
she'll.
Just
this
will
function
as
basically
storage
version
control
and
it'll
notify
openshift
that
hey,
you
need
to
rebuild
and
redeploy.
So
it's
doing
some
heavy
lifting
there,
but
it's
real,
simple
and
there's
almost
nothing
to
it
as
an
application
developer,
I'm
already
used
to
using
app.
You
know,
s2i
on
openshift
and
she's,
already
used
to
using
her
notebooks
and
writing
python
functions
right
cool.
C
B
B
A
Well,
yeah
and
pride
in
your
work
is
everything
right
like
dimming,
going
back
to
dimming,
you
know
having
pride
in
your
work
means
if
you're
a
data
scientist
you
want
to
see
your
models
out
in
the
world
right,
like
that's,
going
to
give
you
the
most
pride
and
and
make
you
feel
the
most
fulfilled,
which
we
all
want
to
kind
of.
You
know
feel
like
we're
being
productive
at
our
job.
I
hope
you
know
and
having
that
desire
to
get
your
models
out
there.
A
I
understand
that
I
I
feel
that
for
a
lot
of
people,
because
I
often
feel
there's
this
wall
of
confusion
that
sometimes
data
gets
thrown
over
and
it
just
doesn't
work
out
somehow
and
we're
trying
to
you
know,
educate
people
so
that
it
can
work
out.
So
this
show
is
for
our
ops
people
and
our
data
scientists
and
our
app
devs
all
together.
All
in
one.
B
So
I'm
in
red
heart,
openshift
data
science.
So
this
is
a
managed
service.
It's
running
on
top
of
openshift
dedicated
and
it's
an
environment
that
like
has
all
the
data
science
things.
So
I
don't
have
to
go
to
the
openshift
console
and
look
at
all
those
dashboards
and
find
a
route
or
a
route
as
you,
north
americans
say,
and
expose
it
and
then
go
you.
E
B
Yeah,
so
this
is
red
how
open
shift
data
science?
You
can
see
that
the
only
thing
we've
got
enabled
here
at
the
moment
is
jupiter
hub,
because
that's
all
our
admin
has
put
putting
our
cluster
for
us
at
the
moment,
but
there's
a
load
of
other
stuff
that
you
know
you
can
add
into
here.
So,
like
chris
talked
about
other
ways
of
serving
models,
you
know
we
could
be
using
a
pipeline,
maybe
we're
using
selden,
deploy
to
serve
and
manage
and
monitor
those
models,
so
that
could
all
be
integrated.
B
B
All
right
so
now,
I'm
just
in
you
know:
standard
jupiter,
lab
environment.
You
know
I
could
launch
a
notebook
and
start
coding
in
here.
I'm
not
gonna,
because
chris
set
me
up
that
git
repo,
I
actually
forked
it
and
already
put
a
load
of
stuff
in
it.
Otherwise
this
would
be
a
very
slow,
very
boring
hour
for
everybody.
Whilst
I
typed
some
slow
python
code,
so
I'm
gonna
copy
my
fork
of
that
and
come
and
clone
it
into
the
environment.
B
All
right,
so
I
know
we've
been
talking
about
jupiter
notebooks
like
if
you
haven't,
maybe
you
haven't
used
them
before.
Maybe
you
haven't
had
the
absolute
delight
of
trying
to
interact
with
them
and
see
them
or
being
sent
one
and
trying
to
get
it
to
run.
This
is
what
they
look
like.
So
it's
essentially
like
a
real
notebook.
It's
like
you,
can
just
put
all
your
thoughts
down,
it's
a
combination
of
posts
and
markdown
and
code,
and
you
can
actually
execute
that
code
in
line
and
then
the
output
is
printed
below.
B
A
C
B
A
B
B
A
B
So
yeah
we're
gonna,
put
together
an
object,
detection
app
that
you
know
you
can
show
a
photo
of
a
dog
and
hopefully
it
will
tell
you.
Yes,
there
is
a
dog
and
you
can
show
a
photo
without
a
dog
and
it
will
say:
there's
no
dogs
here.
So
in
order
to
you
know,
start
this
and
test
this
out.
I'm
gonna
need
a
photo
of
some
dogs,
so
I'm
using
max
and
margot
here
today.
This
is
max.
This
is
margo.
B
They
live
in
bristol
england,
they're,
very
good
dogs,
as
all
dogs
are,
that
is
true,
and
so
I'm
using
tensorflow
as
well.
I
also
tend
to
float
like
a
standard
library.
That's
often
used
for
image
processing,
which
is
what
we're
doing
here,
because
you
know
machine
learning,
models
can't
actually
take
in
a
photo.
They
don't
know
what
to
do
with
that.
So
we're
using
tensorflow
turn
this
into
a
tensor,
which
is
something
more
that
our
model
can
understand.
B
I
said
I'm
using
a
pre-trained
model,
so
there's
like
these
image.
Detection
models
are
good
and
they're,
very
big,
so
to
train
them
yourself.
You
need
tons
of
data
and
you
need
tons
of
time.
You
know
a
load
of
compute
power
and
there's
just
no
benefit
when
someone's
already
trained
a
model
on
dogs
for
me,
so
we're
using
this
ssd
mobile
net
data
model
and
it's
been
trained
on
google's
open
images
data
set,
so
it's
got
600
types
of
objects
that
can
detect.
B
Now
we
just
want
dogs
in
there
so
we'll
deal
with
that
later,
but
yeah
we
load
in
the
model.
We
pass
it
in
our
tensor
here
and
we
can
go
ahead
and
ask
it
to
detect
and
it
spits
out
all
of
this
code,
which
is
essentially
telling
us
the
objects
that
it's
seen
in
this
image,
how
likely
it
thinks
it's
seen
them.
So
this
is,
you
know,
85
79.
B
These
are
much
smaller.
So
it's
not
very
confident
that
seeing
whatever
objective
thought,
this
might
be,
maybe
some
more
classes
corresponding
to
the
type
of
object
detection
boxes.
So
this
model
is
going
to
put
a
box
around
the
thing
in
the
image
to
show
us
where
it
is
and
finally
the
moment
of
truth,
the
things
that
it's
actually
predicted
so
dog,
dog,
footwear
and
so
for
each
of
these
we've
got
a
detection
box.
B
So
we're
going
to
be
able
to
impose
those
back
onto
the
image
so
that
we
can
see
so
we
can
go
ahead
and
draw
bounding
boxes
on
our
image
containing
those
predictions
and
okay,
we've
got
a
dog
here.
We've
got
a
dog
here.
The
font
isn't
very
clear,
but
I
can
tell
you
that
it
thinks
that
their
paws
are
footwear,
which.
B
B
A
B
E
B
A
B
B
B
Right
and
then
the
other
thing
we're
going
to
do
is
filter
out
any
predictions
that
aren't
dog
because
we
only
want
dog.
So
if
we're
only
gonna
get
this
app
to
identify
things
that
are
a
dog
and
that
we
sure
are
a
dog,
so
we
go
ahead
and
you
know
encode
that
here,
if
label
equals
dog
and
score
is
greater
than
0.3,
nothing
too
extreme
going
on,
and
now
we
just
got
our
dogs
and
no
footwear
fantastic.
C
Where
I
end
up
getting
it,
but
that's
why
we
had
that
definition
earlier,
like
hey,
could
you
separate
out
your
prediction
function
into
its
own
python
file?
Right?
Because
you
don't
want
me
fishing
through
that
and
trying
to
figure
out
what
is
important,
because
I
don't
know
I'm
not
going
to
break
her
code
like
I
have
no
idea
which
which
function
is
important
and
the
same
thing
goes
for
dependencies.
You
know,
I
don't
know
if
tensorflow
was
just
for
her
training
or
if
it
was
going
to
use
in
all
of
it.
C
There's
a
bunch
of
those
you
know.
Matplotlib
is
probably
not
going
to
be
used
for
my
prediction
function
right,
so
she
puts
that
requirement
that
I'm
going
to
need
in
the
requirements.txt
and
she
puts
that
prediction
function
in
a
prediction
file.
That'll,
keep
me
from
screwing
up
her
code
and
I'll
be
able
to
incorporate
it
that
much
easier.
Nice.
C
C
That's
my
that's
my
bad.
I
probably
should
have
done
that,
but
six
below
is
kind
of
her
dependencies
for,
like
the
data
science
stuff
and
up
there
was
my
application
dependencies
right,
so
we're
kind
of
keeping
them
separate
and
so
that'll.
Allow
me
to
change
the
application
up
and
work
and,
while
she's
changing
her
data
science
without
messing
with
each
other
too
much.
B
And
then
this
notion
of
putting
the
things
that
we
need
to
make
the
prediction
in
the
prediction.hi,
so
our
notebook
had
everything
in
it
had
let's
look
at
an
image,
let's
see
how
we
turn
an
image
into
a
tensor.
Let's
look
at
all
the
output
from
our
prediction:
let's
plot
all
the
bounding
boxes,
including
footwear.
B
B
C
C
A
C
So
you
can
see
this
is
her
project
that
she
uploaded.
You
know
it's
got
her
new
prediction
file
in
it.
You
know,
which
is
what
we
wanted
right.
It's
got
her
requirements
in
it.
My
application
file
was
ridiculously
simple.
This
is
kind
of
a
dummy
project.
It
it
just
calls
her
prediction
function
right,
and
this
is
a
buildable
s2i
project
like
it
started
off
buildable
right.
She
didn't
have
to
do
anything
and
I
could
have
built
it
right
away
right.
So
we
were
already
ahead
of
the
game.
C
C
Is
really
nice,
like
so
I'll,
show
you
what
it
does,
so
your
openshift
can
just
read
from
that
git
repo
and
create
an
application
from
it.
So
let
me
go
to
the
developer
screen
and
show
you
how
easy
that
is
like
I
I
mean
this
is
really
good
for
development.
I
wouldn't
use
it
for
obviously
production
systems,
but
it's
really
nice
for
iterating
on
right.
If
she
pushes
up
a
change
to
get
or
if
I
push
up
a
change
to
get,
you
know
our
service
gets
automatically
redeployed,
we
can
take
it
and
look
at
it.
C
C
C
C
That's
that's
pretty
much
it,
and
so
every
time
she
pushes
up
a
new
model
that
build
will
go
ahead
and
rebuild
and
redeploy
and
we'll
get
a
new
deployed
service
that
my
app
will
call
and
so
every
time
she
updates
her
model
we'll
see
that
live.
So,
let's
see
how
that
app
works,
real,
quick,
let's
see
all
right.
So
here's
my
little
app,
hey,
frank,
look!
Come
here!
Buddy
come
on
come
here,
everybody.
B
C
It
makes
you
feel
better
as
if
it
makes
you
feel
better.
My
first
time
like
this
is
a
long
time
ago,
but
switching
to
get
from
other
version
control.
I
swear
to
god.
I
think
I
screwed
up
for
six
months,
like
I
think
I
forced
pushed
a
master,
and
I
never
heard
the
end
of
that
for
like
the
rest
of
that
job.
So.
E
B
D
C
No
matter,
no
matter
what
happens
if
it's
good
or
something
else,
I
do
hope
the
data
scientists
save
versions
of
their
old
stuff.
Like
I
have
been
in
that
situation,
it's
like!
Oh
hey,
can
we
get
that
model
and
he's
like?
I
don't
have
it
anymore.
It's
like
it
was
in
my
jupiter
hub
notebook
over
there
and
I
don't
know
where
it
would
happen
to
it.
I
can't
get
it
working
again,
that
is,
that
has
been
frustrating
for
me
too.
B
Yeah-
and
I
think
not
just
the
I
mean
for
me-
it's
not
just
the
model
as
well.
You
know
we
talk
about
the
model
as
being
like
the
entity,
that's
gonna,
but
it's
really
like
it's
a
whole
pipeline.
It's
what
data
trained
that
model.
It's
if
we
think
about
getting
these
stuff
into
applications,
then
you've
gotta.
Add
that
extra
discipline
of
like
you
know
this,
pretend
that
everything
you
do
might
be
audited.
Hey,
which
model
were
you
running
on
dates?
You
know.
C
B
Yeah,
I
think
that's
true.
It's
also
the
case,
sometimes
that
you
know
with
notebooks
you're,
never
you're,
never
able
to
reproduce
the
model,
because
you
know
we're
talking
earlier
about
running
all
the
cells
out
of
order
and
kind
of
you
have
to
impose
some
discipline
to
know
like
what
what
happened
where
things
came
from
yeah
and
how
to
how
to
keep
going.
C
C
All
right,
we'll
think
of
where
we
left
off
so
right
now
this
app
is
fun,
but
it
it
only
like
works
whenever
you
are
clicking
on
it.
So
if
I
want
to
do
a
monitoring
service
like
if
I
wanted
to
monitor
something
outside
for
coyotes
or
for
dogs
or
whatever
to
make
sure
you
know
if
it
gets
on
his
own,
I'd
need
to
be
notified
about
that.
C
So
I
went
ahead
and
made
a
kafka.
Cue
called
object,
detection
right
and
it's
going
to
take
the
images.
Do
the
prediction
and
put
the
objects
on
this
other
cue,
and
then
I
can
read
from
that
object's
queue
and
see.
If
anything
was
detected.
I
can
read
those
predictions
right,
and
so
hopefully
what
we'll
get
is
we
can
put
up
a
camera
and
it'll
take
that
stream
of
images
and
it'll
run
the
prediction
on
those
images
and,
like
I
said,
you've
already
done
your
part.
C
My
goal
is
to
redo
this
app
without
you
having
to
do
anything
right
so
so
I'll.
Let's
go
ahead
and
see
what
I
can
do
so
go
ahead
and
open
up
a
jupiter
hub
app
of
my
own.
So
just
like
you
did
s3
I'm
going
to
go
ahead
and
talk
to
this
kafka
cue.
So
I
need
to
get
the
connection,
information
and
I'll
get
that
here
all
right
and
that
will
go
as
one
of
my
environment
variables
which
I'll
use
in
there
all
right.
C
So
I'm
gonna
go
ahead
and
spawn
my
app,
and
this
is
just
to
show
how
easy
it
is
to
work
with
kafka.
You
can
do
it
from
inside
a
notebook
and
then
we'll
just
see
change
this
app
up
a
little
bit
all
right,
so
we'll
go
ahead
and
do
a
new
project,
a
new
service,
a
tougher
consumer
which
we'll
go
ahead
and
use
from
inside
jupiter
hub
and
we'll
see
what
that
looks
like
all
right.
So
this
is
a
kafka
consumer
which
will
read
from
that
second
queue.
C
So
we'll
go
ahead
and
get
it
working
here.
So
you
can
tell
I've
installed
the
kafka
python
library.
I've
read
my
server
information,
and
this
is
me:
creating
a
consumer.
That's
reading
from
just
a
notebook
test
queue
to
see
if
it
works,
so
it's
subscribed
and
then
here
in
another
notebook
you
can
say,
go
ahead
and
just
send
some
messages
for
it
to
read.
So
this
is
a
producer
right.
C
So
this
is
going
to
put
messages
on
that
cue,
and
so
my
app
was
going
to
go
ahead
and
read
the
images
from
one
queue
and
then
produce
a
new
message
on
another
cube,
and
you
can
tell
this:
one
is
sending
some
hello
messages
and
this
one
is
going
ahead
and
receiving
those
and
consuming
those.
So
we've
got
a
consumer
and
a
producer
worked
out
all
right.
So
let's
go
ahead
and
change
that
up
instead
of
doing
the
rest
rest
service.
C
We
did
before
we'll
just
go
ahead
and
create
a
consumer
to
consume
those
images
that
the
app
is
feeding
into
kafka
and
it
will
produce
the
predictions
on
there.
So
just
do
a
prediction
and
just
stick
that
on
that
other
queue
right
and
so
you've
already
got
your
prediction
hasn't
changed.
It's
still
exactly
the
same.
The
requirements,
your
requirements
are
exactly
the
same
mine.
I
just
changed
to
use
kafka
python.
This
is
kafka,
python
consumer,
so
we
can
go
into
the
app
again
and
we
can
create
a
new
service.
C
C
C
And
there
you
go
so
now,
instead
of
just
talking
to
your
rest,
the
rest
service.
I
can
go
ahead
and
talk
to
this
kafka
consumer
and
I
can
sorry
the
coffee
consumer
is
going
to
be
talking
to
kafka
and
it's
going
to
be
adding
messages
onto
that
queue
which
my
app
is
going
to
pick
up
and
once
that's
done,
building
the
same
way.
Like
the
other
thing,
it's
really
the
same
thing
you
can
take
a
look
at
what
that
does.
C
C
Dog,
so
the
nice
thing
about
this
was
like
the
app
has
totally
changed,
but
sophie
didn't
really
have
to
really
do
anything
at
all
right
and
just
by
that
little
bit
of
structure
up
front
that
you
know
that
that
lends
itself
to
me
being
able
to
change
everything
without
having
to
bother
her
over
and
over
again.
You
know,
she's
got
her
prediction:
she's
got
her
requirements
and
it's
really
easy
for
me
to
pick
up
and
move
with
it.
C
A
The
shared
understanding,
the
shared
knowledge
right
like
that's
what
devops
is
all
about
right
like
helping
others
succeed,
I
feel
like
is
everybody's
role
in
devops
and
ops
in
general,
especially
application
development
right
like
we
should
be
helping
each
other
and
your
dog's
still
in
the
background.
Looking
at
you
like,
why
aren't
you
letting
me
out.
A
Just
want
to
point
that
out
shout
out
to
my
dog,
so
yeah
like
this
was
an
amazing.
You
know
kind
of
hand-off
process
that
we
just
witnessed.
You
know
sophie.
Did
the
work
trained,
a
model
handed
it
over
to
chris
some
shared
understanding
about
what
things
are
needed
and
chris
implemented
it.
You
know
for
lack
of
a
better
term.
So
this
is
the
dream
right.
C
D
C
C
C
C
Right
canary
deployments
and
stuff
yeah
yeah
and-
and
it's
pretty
easy
like
you-
can
just
tell
it
where
your
tensorflow
model
that
serialized
model
is
and
it'll
it'll
be
able
to
deploy
it.
So
it's
real
it's
real
nice
and
easy.
We
can
cover
that
and
then
for
the
building
of
the
model.
You
know.
Sometimes
you
want
it
to
automatically
build
or
build
outside
of
a
notebook,
and
so
people
will
use,
like
you
know
so,
pipeline
of
some
sort,
whether
tecton
keyflow
pipelines,
you
know
argo
whatever
it
is.
C
A
C
Right,
I
mean
you
kind
of
have
to
have
whatever,
because
it
also
depends
on
skill
set
right.
So
if
you
like,
how
much
is
that
data
scientist
willing
to
take
on?
Because
there
are
some
data
scientists
who
feel
pretty
comfortable?
Actually
in
flask
and
this
that
and
the
other
thing,
and
they
don't
mind,
doing
understanding
some
of
that
and
there
are
others
who
don't
want
to
touch
it
so
right.
So
I
think
there's
got
to
be
an
agreement
between
teams
like
hey.
This
is
what
we
can
produce.
C
B
I
think,
just
like
you
know,
a
real
understanding
from
kind
of
the
upside
and
the
depth
side
that
a
lot
of
this
is
outside
of
a
data
scientist
comfort
zone,
like
you
know,
if
there's
data
scientists
that
are
watching
this
they're
just
starting
out
or
you
know,
in
school
learning
data
science
like
nobody,
nobody
seems
to
teach
you.
You
know
recent,
like
package
control
that
library
control,
like
you,
do
just
work
on
your
computer
and
install
every
python
library
into
the
same
environment
like
no
one.
B
The
the
kind
of
the
architecture
and
the
engineering
stuff
is
like
definitely
a
completely
new
level,
and
I
don't
know.
I
hope
people
see
that,
like
you,
don't
you
can
learn
as
much
or
as
little
of
it
as
you
want.
You
don't
have
to
become
an
expert
like
we're
not
trying
to
make
data
scientist
software
engineers.
A
Right
yeah,
that's
the
biggest
thing
right
like
we
got
to
keep
people
close
to
their
disciplines,
but
still
integrate
right,
and
that's
that's
the
struggle.
That's
where
teamwork
comes
in
right
to
figuring
out
where
the
boundaries
start
and
end
for
each
individual
team
or
person
or
whatever
it
may
be,
but
just
get
get
everybody
in
the
room
right
like
have
everybody.
Everybody
needs
a
seat
at
the
table.
Everybody
needs
to
have
that.
C
Having
that
common
starting
point
platform
for
where
they're
working
in
jupiter
or
whatever
helps
a
lot
and
me
being
able
to
look
at
that,
you
know
containerized
image,
even
if
it's
just
a
jupiter
thing
and
seeing
what
dependencies
are
in
that
container,
you
know
helps
towards
production.
A
lot
too
helps
me
pick
up
and
understand.
What's
going
on.
A
B
A
A
Scientists
will
come
on,
but
basically
every
two
weeks,
the
data
services
office
hour
will
feature
storage
or
science
right.
So
when
you
think
data
we're
going
to
have
every
other
week,
it's
going
to
be
alternating
between
like
how
to
set
up
the
storage,
how
to
build
the
environment
to
do
these
things
and
then
have
the
actual
data
science
process
happen
on
the
show.
So,
if
you're
looking
for
chris
blom
or
bloom
and
michelle
de
palma,
they
will
be
here
every
other
week
and
chris
and
sophie
will
be
here
on
alternating
weeks.
A
C
Right,
like
I
think
for
for
our
next
shows,
I
think
I
think
we
got
some
stuff
that
people
want
to
see.
I
think
we
can
look
at
some
data
exploration
from
inside
notebooks,
some
really
nifty
tools
to
do
that.
I
think
we
can
show
people
some
of
the
you
know
the
training
pipeline
stuff
that
people
use
to
build
their
models
yeah,
and
I
think
we
can
show
them
some
of
those
serving
things
I
was
talking
about.
A
Correct
they
are
fun,
and
I
wish
I
was
a
data
scientist
because
it
seems
like
a
lot
of
fun.
So
thank
you
sophie.
Thank
you
chris.
Thank
you
bobby
for
helping
out
the
today
bobby
the
intern
saving
me.
Yet
again.
I
don't
know
what
we're
gonna
do
when
you're
gone
bobby.
I'm
gonna
be
sad
but
tune
in
in
two
weeks
for
more
data
services
office
hours,
but
later
today
we're
gonna
have
in
the
clouds
with
kirsten
newcomer
and
hopefully
andrew
clay,
shafer
talking
about
devsecops
and
shifting
left.
A
We
are
also
going
to
have
devnation
on
the
air.
We'll
have
getoff's
guide
to
the
galaxy
this
afternoon,
we're
going
to
look
into
intuit
intuits,
coming
on,
to
give
us
a
deep
dive
into
argo
and
we're
going
to
wrap
things
up
with
the
another.
You
know
kind
of
series,
premiere
of
the
stack
rocks
community
office
hours
today,
so
stick
around
stay
tuned,
subscribe
to
the
calendar
if
you're
not
already,
which
I'll
drop
in
chat.
Oh
sonny's,
back,
hey
sunny,
come
here,
buddy
come
here.
Come
here.