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Description
Lightning Talks: David Aronchick: Google - Introduction to Kubeflow
A
A
Brandon
has
the
easiest
job
I
have
the
hardest
one,
because
after
this
it's
you
know
evening
activities.
So
you
know
this
is
gonna
be
hard,
but
in
fact
the
funny
part
is
is
I
actually
have
the
easiest
talk
in
the
world,
because
I'm
David,
Ron
check
I
helped
found
the
cube
flow
project,
but
I
basically
do
nothing.
All
these
people
are
doing
this
stuff.
That
makes
cube
flow
great,
we're
just
kind
of
wiring
it
together.
A
You
know
everyone
hears
about
ml,
it's
changing
the
world,
it's
changing
the
dynamics,
eating
everything,
but
the
problem
is,
is
that
most
of
the
world
is
like
this
there's
magical,
AI
goodness
on
one
side,
and
everyone
else
is
on
the
other
side
and
in
between
there's
just
lots
of
pain
and
the
biggest
reason
that
there
is
this
split
between
these
two.
You
know
opportunities
to
go
out
and
get
all
this
great
stuff
and
and
where
people
are
today
is
because
people
have
been
writing
these
incredibly
bespoke
solutions
for
ml.
A
That
you
know
are
not
composable
they're
hard
to
swap
out
the
pieces
that
make
sense
to
you,
or
maybe
your
organization
has
changed,
they're
hard
to
be
portable,
meaning
move
from
your
laptop
to
your
training,
rig
to
your
on-prem
to
cloud
number
one.
The
cloud
number
two:
wherever
the
data
is
and
then
finally
it's
hard
to
scale,
so
you
might
be
able
to
get
it
running
on
a
single
machine,
but
then
to
go
and
do
that
just
like
open
a
I
did
on
2500
machines
is
very,
very
challenging
to
dive
into
each
of
those.
A
Similarly
portability
once
you
get
your
stack
up
and
running
on
top
of
kubernetes,
it
may
be
made
up
of
this
many
layers
or
more
and
when
I
talk
about
that
pipeline
earlier,
that
may
just
be
that
top
portion,
let
alone
everything,
that's
below
it.
And
then
you
go
to
your
training
rig
and
it's
something
completely
different,
and
then
you
go
to
your
cloud
and
it's
something
completely
different
again
and
you're
hit
over
and
over
and
over
again
with
the
various
you
know,
reset
up
and
and
differences
between
those
environments
and
then
finally
scalability.
A
You
know
I
mentioned
already
scaling
via
nodes.
That
is
one
type
of
scalability.
There
are
all
their
scales.
There's.
How
do
you
scale
the
number
of
experiments
that
you
run?
How
do
you
scale
your
teams?
How
do
you
scale
your
data,
all
these
various
things,
those
components
that
are
involved
in
scalability
as
well?
So
you
know,
containers
and
kubernetes
are
pretty
good
at
solving
this,
but
the
problem
is:
is
that
you
end
up
having
to
become
an
expert
in
a
whole
bunch
of
things
as
it
stands
today,
which
is
not
great?
A
So
that's
why
we
introduced
cube
flow.
How
can
we
make
this
overall
system
much
easier
for
you
and
our
mission
here
and
I
say
it
over
and
over
again
make
it
easy
for
everyone
to
learn,
deploy
and
manage
portable
distributed
ml
on
kubernetes?
That
is
not
us
as
part
of
the
cube
flow
project.
Writing
all
this
stuff.
This
is
packaging
and
helping
other
projects
make
their
services
available
in
a
standard
based
way
so
that
you
can
swap
in
and
out
so
that
you
can
scale
them
so
that
you
can't
move
them
from
place
to
place.
A
You
know
around
that
portability
component.
The
way
to
think
about
it
is
that
bottom
section
becomes
all
kubernetes,
that's
the
abstraction
layer
there
and
then
the
section
over
on
the
other
side
becomes
cube
flow
and
you're
able
to
stamp
out
that
cube
flow
in
every
location
that
you
have
today
in
the
box,
and
you
know
on
Friday,
don't
tell
anyone
but
we'll
be
announcing
that
we've
cut
our
0.1
release,
which
we're
very
proud
of.
Thank
you,
but
specifically
in
the
Box.
Today
we
have
Jupiter.
We
have
tensor
flow.
We
have
our
go
for
workloads.
A
We
have
Seldon
core
in
the
box
Daniel.
Is
there
working
very
hard
on
a
pachyderm
proposal
that
we're
very
excited
about?
We
have
reverse
proxy
via
ambassador
and
we'll
be
talking
about
all
the
sorts
of
things
we
have
four
out
of
that
overall
section
up
there.
It's
basically,
these
components
already
have
an
option
in
the
box,
but
you
can
use
many
more
and
we
are
really
are
just
getting
started.
This
is
a
very
small
subset
of
the
people
who
are
helping
out
today
and
we're
really
excited.
A
You
know
the
this
is
I
happen
to
be
from
kubernetes
from
I,
don't
know
de-
10,
and
it
really
feels
like
that
again
I.
You
know
there
were
so
many
when
we
first
got
kubernetes
up
and
running.
There
were
so
many
container
solutions,
so
many
orchestration
solutions.
Everyone
was
just
looking
for
something
to
rally
around
and
that's
what
kubernetes
provided
cube
flow
feels
very,
very
similar.