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From YouTube: OCB: Integrating Data Science and Application Development - Sophie Watson and Chris Chase (Red Hat)
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 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 develops next-generation applications in the hybrid cloud at Red Hat. He is an expert in application development, and has experience in supporting data scientists/
A
A
A
Well,
hello
and
welcome
to
another
openshift
commons
briefing
today
we're
with
chris
chase
and
sophie
watson
and
they're
going
to
talk
to
us
about
integrating
data,
science
and
application
development,
and
I've
had
sophie
on
a
number
of
times
before.
So
you
may
recognize
her
face,
and
chris
chase
is
new
to
new
to
the
openshift
commons
briefing
series.
So
we're
really
happy
to
have
them
both
here
and
I'm
going
to
let
them
both
introduce
themselves.
There's
live
q
a
at
the
end.
B
Thanks
so
much
diane
thanks
for
having
us
and
hi
everybody,
I'm
sophie,
I'm
a
data
scientist
here
at
red
hat
and
over
the
past
few
years
I've
been
really
thinking
about
how
we
can
make
data
scientists
lives
easier.
So
how
can
we
make
their
work
more
efficient,
their
workflows
more
efficient,
and
how
can
we
get
more
of
that
work
that
they
produce
actually
integrated
into
applications?
B
Awesome
so
today
we're
going
to
start
with
a
few
slides
just
to
level
set
on
the
steps
that
are
the
stages
that
are
involved
in
developing
an
application
that
contains
data
science
or
machine
learning
components
from
there.
Hopefully,
we'll
have
enough
information
to
be
able
to
talk
about
some
of
the
pain
points
that
we
see
when
we're
working
together
or
whether
we
talk
to
customers
and
teams
of
data
scientists
and
application
developers
at
our
customers
and
then
finally,
we'll
get
to
the
fun
bit.
B
So
we're
going
to
do
a
demo
today
we're
going
to
be
developing
an
application
which
incorporates
data
science
into
this
application
and
yeah
we'll
be
developing
that
live.
We'll
show
you
a
workflow
that
we've
been
using,
which
is
leaning
on
red
hat,
open
shift
data
science,
which
is
a
new
managed
service
from
red
hat.
So
hopefully
that
sounds
okay
and
I
will
kick
it
off
okay,
so
when
we
think
about
what
it
takes
to
get
machine
learning
into
an
application,
it's
kind
of
assumed
that
we
can
think
about
it
through
these
stages.
B
So
it
begins
with
gathering
and
preparing
that
data
and
exploring
that
data
and
making
sure
that
it's
accessible,
so
you
know
your
data
might
be
everywhere
and
elsewhere.
You've
got
a
gathering,
you've
got
to
federate
it
and
you
might
be
dealing
with
streams.
I
think
later
chris
is
going
to
integrate
some
kafka
into
the
demo.
B
So
that's
kind
of
step.
One
get
hold
of
your
data
step.
Two
we
see
is
developing
the
machine
learning
model.
Now
that
kind
of
sentence
developing
a
machine
learning
model
is,
it
sounds
simple:
there's
actually
a
lot
of
work
going
on
behind
the
scenes
there
so
you've
got
to
explore
your
data.
You've
got
to
carry
out
future
engineering
techniques
to
extract
information
from
that
data
that
you
think
is
going
to
be
able
to
answer
your
question
at
hand.
You've
got
to
be
able
to
solve
some
problem,
and
so
your
data
needs
you
know
extracting.
B
B
You
can
go
ahead
and
think
about
training
or
working
with
a
machine
learning
model,
so
in
general
we
think
of
training
a
machine
learning
model
as
like,
allowing
that
model
to
learn
some
underlying
parameters
based
on
the
data
that
you
show
it
in
the
example
we'll
be
using
later
we're
going
to
use
a
pre-trained
model,
so
there's
lots
of
pre-trained
models
on
the
internet.
Just
waiting
for
people
to
you
know,
they're,
open
source.
You
can
just
go
and
download
them
and
you
could
use
them
in
your
application.
B
So
we'll
see
what
that
process
looks
like
as
well
later,
once
I've
got
a
model
as
a
data
scientist,
we
just
deploy
it
in
an
application,
simple
right.
So
in
the
olden
days,
I
would
throw
this
model
over
the
wall
to
chris,
who
would
then
try
and
reproduce
what
I've
done
and
create
an
application
that
contains
this
machine
learning
component
and
once
that
application
is
up
and
running,
we
need
to
continuously
monitor
and
manage
it.
B
So
when
you
deploy
a
traditional
application
without
any
intelligence
or
machine
learning
components,
then
there's
kind
of
a
few
ways
that
things
can
break.
But
typically,
when
things
break
you
get
an
error
message
or
your
system
stops
working
machine
learning
models
are
often
a
little
bit
different.
They'll
continue
to
spit
out
predictions.
B
It
just
happens
that
the
prediction
might
become
wrong
more
often,
so
this
could
be
due
to
model
drift
or
data
drift,
where
the
data
that
you
trained
the
model
on
is
no
longer
representative
of
the
data
you're.
Seeing
in
the
real
world,
we
wouldn't
want
to
train
a
stock
market
model
for
predicting
the
stocks
tomorrow
on
data
from
the
1800s,
because
it's
just
a
completely
different
world
to
what
it
was
there.
B
So
this
pipeline
is
usually
carried
out
by
a
range
of
people,
as
a
data
scientist,
I'm
most
comfortable
with
these
bits
in
red,
so
gathering
preparing
data
and
then
carrying
out
feature
engineering
and
deploying
that
model,
and
I'm
also
happy
to
monitor
the
model,
detect
this
drift
and
push
out
any.
You
know
update
my
model
if
I
need
to
so
that
changes
can
be
pushed
out
to
the
system,
but
the
part
of
the
system
that
I'm
really
find
the
least
comfortable.
Is
this
one
here
deploying
that
model
in
an
application?
B
So
it's
not
ideal
to
work
on
your
own
laptop
as
a
data
scientist,
but
a
lot
do
so.
The
solution:
well,
let's
just
push
data
scientists
to
the
cloud
fantastic:
they
can
get
access
to
the
hardware
they
need.
They
can
see,
use
more
storage.
You
can
access
that
sensitive
data,
that's
behind
company
firewalls.
B
Sadly,
it's
not
quite
so
easy,
so
data
scientists
in
general
have
a
different
range
of
skills
to
application
developers
and
software
engineers,
and
so
the
transition
to
cloud
computing
is
really
not
simple
and
it
as
as
a
data
scientist,
I
don't
know
much
about
infrastructure,
I
don't
really
care
about
infrastructure.
I
just
want
to
access
it,
so
I
think
in
general,
data
scientists
are
looking
for
higher
level
tools
than
the
things
that
are
usually
used
in
standard
software
engineering
practice.
B
So
if
you
haven't
seen
a
jupiter
notebook
before
they
look,
something
like
this
they're,
a
mixture
of
prose
and
code,
and
you
can
execute
the
cells
of
code
and
the
output
is
printed
in
line
in
your
notebook,
so
they're
a
nice
communication
tool,
you
can
kind
of
document
what
you're
doing
see
what's
happening,
and
this
is
where
I
go
ahead
and
develop
my
machine
learning
techniques
but
in
practice
they're
not
as
reproducible
as
you'd
like
them
to
be
so.
Actually,
you
have
no
idea
how
your
colleague
ran
the
cells
in
the
notebook.
B
If
I
pass
this
over
to
chris-
and
I
say,
hey,
I
trade
my
model,
it's
in
the
notebook,
he
doesn't
know
whether
I
ran
the
cells
in
order,
or
maybe
I
skipped
a
cell
he's,
never
going
to
be
able
to
reproduce
the
results
that
I
got
chris.
I
hear
you've
got
a
few
other
reasons.
Why
you're
not
a
huge
fan
of
jupiter
notebooks.
C
Oh
my
gosh,
yes,
I
mean
usually
when
I
get
a
notebook
from
a
data
scientist
that
I'm
trying
to
convert
into
an
intelligent
app.
You
know,
there's
a
bunch
of
dependency
installations,
there's
no
version
locking
for
those,
and
so
I'm
trying
to
tease
out
the
important
dependencies
and
if
one
version
is
off
of
just
a
sub
component
that
isn't
locked
down.
You
know
it's
just
a
total
beast
to
figure
out
which
one
is
going
to
work
for
this
data
scientist
and
I'll,
ask
him
and
that
one
doesn't
work,
it'll
conflict
with
something
else.
C
C
In
addition,
like
usually
like
the
exploration,
training
and
the
actual
predictions
are
all
kind
of
mixed
in
that
same
notebook
and
I'm
just
trying
to
tease
out
maybe
that
one
prediction
function
I
need
so
all
of
this
is
merely
me
trying
to
tease
out
like
one
function
and
like
the
proper
dependencies,
and
it's
just
never
written
that
way.
Sophie.
B
B
So
what
we're
going
to
show
you
today
is
an
example
of
how
we
can
do
a
better
job
at
that
we're
going
to
be
using
red
hat
openshift
data
science,
which
is
the
new
managed
service
from
red
hat.
So
this
is
targeted
at
data
scientists
and
anybody
involved
in
the
development
and
management
of
intelligent
applications.
B
C
I
do
my
dog
at
least
I
think
it's
my
dog
is
digging
up
my
flower
bed,
I'm
not
100
sure,
because
I
have
never
caught
him.
Never
he
is
a
very
sneaky
dog.
So
I
would
like
to
make
an
app
that's
going
to
alert
me
when
my
when
a
dog
goes
into
my
flower
bed.
So
I
got
I
need.
I
got
the
app
kind
of
worked
out,
but
I
need
something
that's
going
to
detect
dogs
and
images.
Can
you
can
you
do
that.
B
Yeah
sure
so,
there's
tons
of
machine
learning
models
online
ready
for
use
that
can
detect
specific
objects
trained
on
data
sets.
That
include
dogs.
So
we
can
definitely
take
one
of
those
download
it
explore
it
test
out
on
some
data
and
make
sure
it
works.
So
should
I
just
go
ahead
and
start
doing
that,
but.
C
Let's
slow
down
a
little
bit
like
you
said
we
could
use
a
little
bit
of
structure.
I
mean
there's
a
ton
of
solutions
that
are
really
really
advancing.
They're
awesome.
We
love
them
with
ai
teams.
We
could
be
taking
your
model
building
it
on
kubeflow
tecton
pipelines,
putting
it
on
s3
downloading
that
from
s3
using
another
pipeline
and
maybe
seldom
deploying
it
and
kf
serving
to
it.
But
I
don't
want
to
do
that
because
it's
just
you
and
me
sophie.
I
don't
have
a
whole
it
group
supporting
me.
C
C
So
what
we
have
is
just
red
hat,
openshift
data
science,
your
jupyter
notebooks
in
particular,
and
I'm
going
to
be
working
on
my
own
environment,
we're
going
to
be
deploying.
We
already
have
openshift
so
using
a
normal,
very
popular,
open
shift,
developer
pipeline,
the
s2i
on
cluster,
build
from
git.
C
We
can
just
still
get
our
work
done
so
I'll,
be
working
on
my
ide
you'll
be
working
on
your
notebooks,
we'll
push
up
to
that
same
git
repo!
You
know,
openshift
will
get
those
notifications
rebuild
every
time
you
push
up
a
new
model
and
redeploy
it
for
me
and
I'll
just
continue
using
that
application,
and
so
let's
talk
about
that
structure
a
little,
so
we
have
a
little
template
here,
which
is
the
most
basic
buildable
python
thing
ever
and
I've
added
a
little
bit
to
it.
C
So
this
is
just
a
normal
application,
python
project
that
will
build
and
deploy
from
s2i
from
git.
So
it's
got
an
application
file,
this
wsgi.com,
and
that
is
really
it
there's
a
little
bit
of
a
configuration.
But
for
you
all
you're
going
to
have
to
worry
about
is
a
couple
other
files.
First
of
all,
I
just
put
in
some
sample
notebooks.
C
You
can
forget
them
if
you
need
to,
but
that's
just
where
you
can
start
getting
used
to
using
notebooks
and
then,
if
you
could
tease
out
that
prediction
function
as
a
standalone
and
put
it
in
a
python
file.
That
would
help
a
whole
lot
and
then,
if
you
make
sure
that
you
lock
down
those
dependencies
and
separate
them
into
a
requirements
file,
that's
that's
game
will
be
way
out
of
the
curve
so
build.
C
Account
type
today
and
we'll
go
ahead
and
get
this
to
you.
Let
me
go
ahead
and
create
it
all
right.
So
now
I
am
going
to
get
this
repo
link,
I'm
going
to
go
ahead
and
send
this
to
you
and
then
we'll
be
ready
to
roll
all
right.
Let
me
send
that
to
you
all
right
up
to
you
sophie
go
ahead
and
let's
see
what
you
got.
B
B
Fantastic,
hopefully
you
can
see
a
web
browser.
Let
me
know
if
you
can't
chris,
so
we
are
in
red
hat
openshift
data
science
now.
So
this
is
where
I'm
going
to
work
today-
and
this
is
my
environment
for
the
day.
So
this
is
the
launch
page.
This
is
what
you
see
when
you
first
log
in
as
you
can
see,
we've
only
got
one
application
enabled
here
today
and
that's
jupiter
hub.
B
So
what
we've
got
here
is
a
range
of
self-managed
partner,
managed
and
red
hat
managed
offerings
that
integrate
with
red
hat
openshift
data
science.
So
you
can
see
you
can
use
anaconda
commercial
edition
to
manage
your
packages,
ibm
watson
studio
if
you're
looking
for
an
aiml
suite
open
shift
streams
for
apache
kafka,
chris
we're
going
to
use
that
later
right.
B
You
can
also
install
selden,
deploy
so
and
access
that
say
seldom
deploy.
Is
software
for
managing
your
machine
learning
models
once
you
put
them
into
production
so
that
final
stage
I
talked
about
when
we
manage
and
monitor
the
models.
Sultan
has
lots
of
tools
to
help
with
that,
as
well
as
with
the
deployment
stage
so
to
go
from
code
to
an
application.
B
B
B
Okay,
so
when
I
click
launch,
I
get
taken
to
this
page
here
start
a
notebook
server.
So
what
you
can
see
is
I've
got
a
few
different
options,
we'll
talk
about
them
now.
So
if
we
start
at
the
top
with
the
notebook
image,
a
notebook
image
is
just
a
container
that
contains
the
python
libraries
that
we
might
want
to
use
in
our
notebook.
So
you
can
see
here.
I've
got
six
notebook
images
to
choose
from.
You
can
tell
from
their
names,
perhaps
what
they
are
and
we
can
click
to
get
a
bit
more
information.
B
So
you
can
see
here
that
this
tensorflow
notebook
image
contains
tensorflow
version
2.4.1,
as
well
as
all
of
these
other
packages
and
some
more
standard
packages
as
well.
Now
the
nice
thing
about
these
notebook
images
is
that
they've
been
vetted
by
red
hat.
So
we've
checked
that
all
these
library
versions-
I
say
we
had
nothing
to
do
with
it.
My
fantastic
team
have
checked
the
library
versions
here,
check
that
everything
works
well
together
and
are
able
to
go
ahead
and
provide
that.
B
So
I
don't
have
to
worry
about
setting
up
my
own
managing
my
own
libraries
and
dependencies
and
versions
everything's
going
to
play
nicely
together,
we're
going
to
use
tensorflow,
it's
commonly
used
for
image
processing
and
today
we're
going
to
be
dealing
with
images
of
dogs.
So
I
guess
that
makes
sense
deployment
size
here.
I
can
select
the
size
of
the
container
that
I
want
to
spin
up,
so
you
can
see
that
I've
got
a
range
of
options
here.
B
These
have
been
set
for
me
by
it
and
based
on
what
we
have
in
this
cluster
and
I'm
going
to
use
a
medium
container
size.
So
image
processing
is
often
really
computationally
expensive,
but
the
the
most
computationally
expensive
part
is
training
the
model
and
because
we're
going
to
be
using
a
pre-trained
model.
Today
we
don't
have
to
worry
too
much
about
that,
so
this
medium
container
should
be
fine.
B
I
can
also
add
some
environment
variables
here
we're
going
to
connect
to
data
in
s3,
and
so
I've
added
my
aws
access
key
and
secret
access
key
and
it's
actually
remembered
those
from
last
time.
I
was
here
so
I
am
gonna
not
spawn
this
server,
but
instead
just
step
over
to
one
that
I've
already
got
running
like
a
good
cooking
show,
and
here
we
are
okay.
B
So
this
is
what
you
see
once
you
launch
your
spawner,
so
we
can
go
ahead
and
make
a
jupiter
notebook,
but
chris
wanted
me
to
start
with
a
set
of
files.
That's
in
that
format
he
laid
out
in
that
git
repo,
so
I'm
gonna
go
ahead
and
clone
that
git,
repo
and
actually,
like
all
good
cooking,
shows
I've
already
done
quite
a
lot
of
the
work
and
developed
this
app.
So
let
me
just
clone
my
fork
of
chris's
repo
and
go
ahead
and
show
you
what
we've
been
doing
in
terms
of
data
science.
B
B
And
the
charm-
I
don't
know
why
this
was
work-
will
work
because
it
didn't
work
first
time
you
never
know
thinking
about
it:
fantastic,
okay,
so
crisis,
a
bird
we're
in
business,
I'm
a
data
scientist
that
knows
how
to
clone
get
repos,
so
we're
able
to
kick
off.
So
you
can
see
that
it's
added
this
doctor
detector
service
here
into
my
environment,
thanks
everything
that
was
in
that
git
repo.
We
can
go
ahead
and
have
a
look.
B
B
B
B
So,
let's
see
if
we
can
get
a
model
to
detect
them
well
in
order
to
get
a
machine
learning
model
to
detect
them,
we
can't
just
pass
it
an
image.
Machine
learning
models
need
things
in
a
specific
format:
we're
using
tensorflow
today,
so
we're
going
to
have
to
transform
it
into
a
tensor.
So
we
take
this
image,
transform
it
into
a
tensor
which
looks
something
like
this
means
nothing
to
me,
but
the
computer
is
happy
and
we're
also
going
to
load
in
a
model
that
we
want
to
use.
B
So,
like
I
said,
there's
loads
of
pre-trained
object,
detection
models,
there's
no
point
as
training
our
own
for
this
purpose
and
the
model
we're
going
to
use
today
is
the
ssd
mobile
net
v
2.1
model,
and
you
can
download
that
yourself
from
tensorflow
hub
have
a
go
play
around
with
it.
This
is
trained
on
an
open
data
set
from
google
and
it
can
recognize
600
types
of
objects,
so
not
just
dogs,
it's
trained
on
600
things,
one
of
which
is
dogs.
B
So
we
pass
that
tensor
into
our
model
and
ask
it
to
predict
what
is
in
the
image
and
it
spits
out
a
result
that
looks
like
this.
This
output
here,
that's
in
nine.
What
we've
got
here
is
some
cache
of
the
detection
class
name.
So
hopefully,
this
hashes
to
hashes
from
dog,
we'll
see
in
a
moment
we've
got
some
detection
scores,
so
the
model
when
it
identifies
something
is
able
to
say
how
confident
it
is
in
that
prediction
that
it
made.
So
you
can
see
here
it
had.
B
You
know
over
80
confidence
in
its
first
prediction,
just
shy
of
80
in
the
second
and
then
there's
a
real
drop
off
down
to
0.2
percent
confidence.
It's
got
all
of
these
objects.
It's
detected
and
we
can
see
the
ones
near.
The
bottom
are
really
detected
with
negligible
confidence.
So
the
model
is
not
so
sure
about
those
we've
got
labels
again
corresponding
to
the
object,
that's
predicted.
So
you
can
see
the
first
two
are
the
same
four
four
six,
four
four.
B
Six
and
after
that
everything
looks
kind
of
like
a
four
three
four
we've
got
detection
boxes
so
object.
Detection
models
are
able
to
identify
where,
in
the
image
these
objects
are
so
for
each
of
the
objects
detected.
It
gives
you
four
coordinates
which
define
a
box
which
will
enclose
the
object
and
finally,
we've
got
some
human,
readable
detection
class
entities
and
you
can
see
the
first
two
predictions
were
dog,
so
this
is
what
the
model
splits
back
to
us,
but
this
isn't
that
much
use.
B
What
I
would
like
to
do
is
embed
this
information
in
the
image,
so
we
can
see
it.
So
that's
what
we
do
here.
We
plot
the
bounding
boxes
that
we
were
given
the
coordinates
for
above
and
look
at
this
okay,
so
you'll
have
to
take
my
word
for
it
when
this
says
dog-
and
this
says
dog,
so
we've
successfully
identified
max
and
margo,
but
we've
also
got
some
extra
things
going
on
in
this
model.
B
You
can
see
here
that
the
dog's
paws
have
been
identified
as
footwear,
which
I
think
is
kind
of
adorable,
but
if
we
just
want
this
dog
detector
to
only
detect
dogs,
so
that
chris
doesn't
get
woken
up
in
the
middle
of
the
night
when
someone's
walking
through
his
lawn
with
boots
on,
we
want
to
filter
out
everything
that
isn't
a
dog.
Similarly,
you
know
if
chris
has
a
camera
set
up
and
a
bird
flies
past.
We
don't
want
it
to
tell
chris
that
it's
found
something.
B
We
only
want
to
say
when
we
found
a
dog,
so
we
do
a
little
bit
of
work
here
to
only
accept
things
if
the
label
is
dog
and
the
model
is
more
than
30,
confident
that
it's
a
dog,
hopefully
that
won't
wake
you
up
in
the
middle
of
the
night
chris
and
you
can
see
here-
we've
no
longer
got
any
footwear
identified
in
this
image.
It's
just
two
dogs.
B
B
Okay,
so
I've
already
pulled
my
prediction
code
into
the
prediction.pie,
so
this
is
everything
from
the
notebook
that
you
need
to
make
a
prediction,
so
we
need
the
model
and
then
we've
just
got
some
simple
code
here.
This
is
pulled
from
the
notebook
and
you
can
see
here
we're
restricting
only
two
things
that
are
dog
and
with
probability
of
more
than
0.3.
B
C
B
B
C
I
certainly
hope
so,
or
else
whoever's
watching
this
is
gonna,
be
severely
disappointing.
Let
me
see
if
I
can
share
my
screen
here.
Okay,
so
let's
see
what
you
got
all
right,
so
here's
the
dog
detector
service
app.
You
have
pushed
up
changes.
I
see
them,
including
there's
your
protection
file,
perfect
and
yep.
There's
your
requirements
and
with
those
two
things
see
this
thing
I
kind
of
cheated
it
already
built
when
I
even
gave
it
to
you.
C
So
hopefully
it's
going
to
be
super
easy
to
build
now,
let's
go
ahead
and
give
that
a
try
all
right.
So
here
is
my
project
that
I
already
have
like
my
application
in
I'm,
going
to
put
your
service
in
that
same
application
project
space.
So
let's
go
ahead
and
add
this
application
used.
Let
me
clone
this
all
right,
so
this
is
actually
going
to
be
super
familiar
to
any
developer.
Who's
used
to
working
on
openshift
we'll
go
ahead
and
add
a
project
add
an
application
from
git.
C
C
Okay,
there
it
is
so
there
it
is,
it's
still
building
which
is
fine
lit,
but
you
know
one
other
thing
we
want
sophie
is
I
want
my
service
to
get
updated
every
time
you
update
your
model
because
maybe
we
need
to
have
a
lower
threshold.
Maybe
you
need
to
do
like
point
two
thresholds
for
dogs.
Maybe
0.3
is
too
high
or
whatever
you
want
to
change,
so
let's
go
and
make
that
happen.
B
C
Exactly
so
every
time
you
push
up
that
model,
I'm
going
to
call
into
your
updated
service.
So
it's
pretty!
Oh,
let's
see
if
we're,
let's
see
if
our
application
is
working
now,
okay,
here
it
is,
let's
give
this
a
try.
I
don't
have
a
dog
with
me
right
now.
I
have
fake
dogs
on
my
phone.
Give
me
a
second
here:
that's
it!
That's
the
culprit,
pretty
good!
Oh,
let's
see
if
I
can
fake
it
out
a
little
bit
with
this
much
worse
dog
nope,
let's
see
nope,
this
dog
is
not
oh
there.
He
goes.
C
I
got
it
at
27,
not
a
great
dog
just
enough.
We
might
want
to
move
that
threshold
wrong.
I
don't
know
if
we
want
it
to
show
up
or
not,
but
I
like
it,
so
your
stuff
works
great.
It
perfect,
like
really
really
good,
although
I
hate
to
say
it,
I
my
app
kind
of
sucks
yeah,
that's
a
problem,
see
I'm
gonna
have
to
push
this
button
to
take
pictures
and
call
into
your
app
that's
not
exactly
the
monitoring
solution
I
was
looking
for.
C
I
think
we
need
to
fix
our
app
because
right
now
we
have
this
build
where
my
application
is
just
calling
into
a
rest
api
whenever
I
trigger
it.
What
I
think
we
want
to
do
is:
I
think
we
want
to
go
ahead
and
stream
these
images
up
and
do
predictions
on
all
of
them,
and
then
I
get
an
alert
when
it
has
a
dog
in
it.
So
I
think
for
what
we
want
to
do.
I
think
we
want
to
use
kafka.
C
B
B
C
C
So
the
first
thing
I'm
going
to
do.
I
think
we
need
a
coffee
cue,
so
I
don't
really
want
to
manage
it
myself,
because
I'm
an
application
developer,
not
an
infrastructure
guy.
So
much
so
I'm
going
to
go
ahead
and
let
red
hat
manage
this
for
me.
So
I
went
ahead
and
created
a
kafka
queue
on
red
hat
openshift
streams
for
apache
kafka,
and
so
this
way
I
don't
have
to
really
mess
with
it.
I
created
a
few
topics
here
I
created
an
images
topic
for
those
images.
C
C
All
right,
you'll
see
here,
I'm
going
to
try
and
use
the
same
notebook.
You
did
the
tensorflow
notebook
medium
size
and
I'm
going
to
add
in
my
environment
variables.
I
don't
need
aws.
What
I
need
is
that
kafka
information.
So
let
me
go
ahead
and
get
started.
You
can
see
that.
Let
me
go
ahead
and
start
the
server
I
am
not
so
well
prepared.
I
am
not
a
cooking
show,
unfortunately,
so
I'm
just
spawning
this.
For
you
sorry.
C
So,
let's
see
if
we
can
go
ahead
and
connect
to
that
kafka
cube
from
my
jupiter
notebook
here,
so
we
can
get
started
all
right.
So
first,
I
need
to
connect
to
it
and
just
read
anything:
that's
being
pushed
up.
You
can
see
here
I'll,
go
ahead
and
start
this
whole
thing.
I've
got
the
connection
information
here
from
those
environment
variables
I
injected
and
I
get
to
use
those
and
that's
my
topic
there
right
and
so
here's
a
just
a
real
dead,
simple
consumer
written
in
python
that
we're
going
to
be
checking
out.
C
C
C
C
We
want
to
go
ahead
and
consume
messages
from
one
topic
and
put
them
on
another
produced
after
we
call
your
prediction
function
and,
as
you
can
see,
I
haven't
changed
your
prediction
function
at
all
and
I
have
changed
my
dependencies
for
python
instead
of
flask,
but
your
dependencies
are
still
the
same
and
right
now
we
can
go
ahead
and
build
this
app.
So
I
totally
changed
the
app
and
you
haven't
done
anything
so
that's
kind
of
nice.
C
C
Jupiter
notebook,
so
really
it's
going
to
be
the
same
thing.
Oops,
I'm
going
to
put
that
in
there
now.
I
don't
want
to
show
you
guys
my
username
and
password,
so
I
already
have
that
working
because
I'm
cheating
and
we're
going
to
go
ahead
and
go
straight.
So
this
is
what
it
would
look
like.
So
here
is
the
consumer.
That's
been
built
and
is
ready
to
go.
Let's
see
if
this
works
all
right,
let's
go
ahead
and
go
to
the
app.
C
C
C
B
B
That's
so
cute
franklin's,
so
adorable,
okay,
so
thanks
everyone
for
sticking
with
us
today.
What
you've
seen
is
some
of
the
pain
points
around
integrating
machine
learning
into
application
development,
and
then
we
showed
you
a
workflow
where
chris
just
did
a
little
bit
of
pre-work
to
set
up
a
couple
of
files
for
me
in
this
git
repo,
and
it
enabled
my
work
to
be
more
efficiently
and
effectively
incorporated
into
this
application.
B
A
Questions,
thank
you
for
that
and
now
I
know
why
your
twitter
handle
should
be
chase
red
hat,
because
franklin
is
who
you
should
use
as
your
icon
for
twitter
chris.
So
I
think
you've
you've
you've
got
a
new
new
show
star
that
in
this
demo,
wonderful
demo-
and
it
made
me
actually
realize
that
even
I
probably
could
create
a
data
science
application
now.
A
This
was
really
amazing
because,
like
I'm,
I'm
stuck
in
jupiter
notebook
land
and
handing
off
jupiter
notebooks
to
application
developers
is,
is
never
successful
or
never
a
good
past
production
shall
I
say,
but
it
is
an
amazing
way
to
develop
models
and
to
work
with
them
and
understand
them
better.
So
I
think
that's
really
the
nice
juxtaposition
of
these
two
conversations
that
you're
having
with
chris
and
with
yourself
sophie.
So
I
really
appreciate
that.
So
that's
that's
awesome.
A
There
was
one
question
early
on
that
I
thought
was
actually
pretty
good
is
where,
where
do
most
data
science
application
development
projects
fail,
and
I
think
you
what
I
said
to
the
person
who
was
asking
is
I'm
pretty
sure
we're
going
to
cover
that
in
this
talk,
and
I
think
it
is
it's
the
for
me.
It's
always
the
handoff
between
the
data
scientist
and
the
application
developer
and
making
that
service
available
as
easily
as
possible,
so
that
chris
doesn't
have
to
track
down
sophie
to
fix
a
model.
A
That's
you
know
not
broken,
but
maybe
he
needs
a
higher
percentage
because,
as
we
saw,
the
stuffy
dog
often
got
recognized
so
tweaking
that
little
bits
it
really
this.
This
workflow
that
you've
come
up
with
between
the
two
of
you
or
the
three
of
you
with
franklin
works
very
nicely,
and
I-
and
I
think
you
did
answer
that
question,
but
I
wanted
to
put
that
to
both
of
you
is:
is
that
where
you
see
the
breakdown
happening
for
application,
development
for
data
scientists
working
with
application
developers.
B
That's
certainly
where
I
see
it
both
for
me
and
for
a
lot
of
the
data
scientists
that
we
talk
to
in
our
daily
work,
creating
a
machine
learning
model-
and
you
know
developing
a
data
science
technique
or
training
a
model
is,
is
what
data
scientists
are
trained
at.
That's
what
we
do,
that's
what
we
enjoy
to
do,
but
the
application
development
part
is
just
a
completely
different
world
and
a
lot
of
data
scientists
have
kind
of
mathematical
backgrounds
rather
than
computer
science,
backgrounds
and
they've
just
got
a
completely
different
skill
set.
B
So
I
think,
there's
kind
of
two
things
to
do.
One
is
try
to
bring
structure
and
discipline
to
the
data.
Science
workflow
and
we've
got
a
lot
that
we
can
learn
from
software
engineers,
and
I
think
you
know,
there's
a
lot
of
similarities
between
the
things
we
do
and
the
way
that
we
work,
and
so
I
think
we
can
steal
some
of
their
tools
and
their
techniques.
You
know
even
up
until
kind
of
five
years
ago,
data
scientists
didn't
use
git
at
all.
B
Now,
maybe
20
are
happy
using
it
and
a
few
use
it
reluctantly
and
the
rest
still
not
so
much,
but
just
by
using
those
tools
and
integrating
ourselves
into
the
application
development
life
cycle,
we
can,
you
know,
show
how
we
can
get
these
machine
learning
models
into
a
application
chris.
What
do
you
think.
C
I
mean,
I
think,
that's
true.
I
think
I
think
we're
trying
to
allow
each
group
to
focus
on
their
specialty
so
that
we
do
a
good
job
and
allow
some
sort
of
abstraction.
So
we
can
let
them
iterate
independently
and
still
not
break
everything,
and
this
was
like
a
really
dead,
simple
way
of
doing
so,
but
in
their
like
way
more
advanced
ways
we
could
maybe
use
s3
to
trigger,
builds
and
do
a
bunch
of
other
stuff.
A
Helps
a
lot
I
think,
awesome
well,
I
do
just
want
to
thank
you
both
for
your
time.
Awesome
demo,
you
rocked
it
and
I
actually
now
think
I'm
going
to
go
in
and
try
and
do
the
demo
myself
and
I
encourage
everybody
else,
who's
done
it
do
you
have
a
repo
with
some
of
this
stuff
tucked
away
somewhere
that
you
can
share
with
us.
C
Yeah,
it's
not
even
tucked
away
it's
right
on
the
front
page.
Let
me
get
that
for
you
right
now,
so
this
one
here
it
is:
let's
go
to
the
rrh
versus
bu,
the
dock
detector
setup.
Will
you
will
have
to
install
sternzy
and
then
you
just
run
the
setup
and
then
it
should
be
running
in
a
single
namespace,
the
kafka
queue
and
the
three
parts
of
the
application.
C
A
Awesome
well
thanks
again
guys,
I'm
not
seeing
any
other
questions,
but
I'm
sure
there's
going
to
be
a
few
people
who
are
excited
about
this
and
besides
me
and
we'll
be
trying
to
do
this
as
well
and
find
the
dog
that's
digging
up
our
garden
beds
or
or
whatever
that
animal
is
so
that'll
be.
The
trick
is
to
do
variations
on
this.
So
thanks
very
much
everybody
and
we'll
have
you
back
again
soon.