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From YouTube: CDF - SIG MLOps Meeting 2021-05-06
Description
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A
A
B
A
Well,
I
don't
know
if
we're
gonna
get
anybody
else
joining
us
today.
B
Yeah
I've
had
this
on
my
calendar
for
a
while
and
wanted
to
attend
and
learn
about
what
was
going
on
with
this
sig.
B
A
Yeah,
so
so
this
particular
session
is
one
that
was
originally
used
by
the
the
kubeflow
team
and
they
seem
to
have
moved
elsewhere.
A
B
Yeah
general
cube
flowy.
I
am
I'm
in
the
kubernetes
and
openshift
world
to
work
for
red
hat
on
consulting
so
been
using
our
products,
but
was
curious
to
see
what's
happening
in
the
community
around
yeah
and
in
general,
and
see
where
I
can
get
involved.
B
C
A
Yeah,
so
I
I'm
responsible
for
the
the
ml
ops
roadmap,
so
so
we
we've
been
recently
starting
the
sort
of
2021
update
on
that.
I
don't
know
if
you've
seen
the
roadmap
document.
B
Yes,
I
have
the
the
public
github.
A
That's
correct,
yeah
yeah,
so
I
mean
you
know
I
I
don't
know
if
you
have
any
views
on
that
or
if
that's
something
that's
useful
to
you
in
any
way.
B
Yeah,
I
think
I
last
looked
at
it
earlier
in
the
year
late
last
year.
Let
me
take
a
look
at.
A
B
A
No
major
change
this
year
yet
so
we
published
things
around
september
october
time,
so
it's
probably
not
changed
much
since
you
last
looked
at
it.
B
Yeah,
when
I
did
look
at
it,
I
thought
the
yeah
scope
and
approach
in
terms
of
challenges
I
see
was
general
challenges
to
ops
and
teams,
transitioning
from
thinking
about
yeah,
cicd
and
traditional
application
sense.
Having
having
to
go
to
those
considerations,
it
seemed
like
a
really
good
place
to
focus
the
group
yeah
when
I
was
looking
at
that.
A
I
spent
a
bit
of
time
couple
weeks
ago
at
at
one
of
the
ml
ops
conferences.
A
And
there's
there's
a
very
big
divide
between
people's
understanding
of
what
ml
ops
actually
involves
and
I
think
there's
a
there's
a
lot
of
education
and
training
that
needs
to
happen
to
really
get
people
building
sort
of
high
quality
production
assets
in
the
machine.
Learnings.
B
Yeah,
starting
with
I've,
seen
that
too,
and
starting
with
that
foundation
of
more
so
clean
data
and
data
science
best
practices
and
making
sure
that
you're
starting
there
before
even
looking
at
these
tool
chains,
I
I
found
that
to
be
one
thing
that
is
still
glossed
over,
sometimes
with
with
teams.
I've
worked
with
so
far,
but
it
the
tools
are
definitely
newer
and
are
yeah
open
shifts.
B
I
guess
openshift
red
hat's,
open
data
hub
efforts
are
still
relatively
new
and
and
seeing
how
teams
are
adopting
that
a
number
of
the
smaller
tools
like
the
jupiter
hub
and
data
science,
as
a
service
type
approaches,
we've
seen
quite
a
bit,
but
in
terms
of
thinking
about
more
so
overall
methodology.
I
I
do
see
this
as
yeah
early
days,
so
I
was
excited
to
see
that
sagan
and
see
what
was
going
on,
but.
A
Yeah,
so
I
mean
the
what
we're
trying
to
to
do
with
the
roadmap
is:
make
it
clearer
that
machine
learning
assets
don't
safely
stand
on
their
own,
that
you?
What
you're
building
is,
is
not
a
piece
of
machine
learning,
equipment,
you're,
building
a
product
and
that
product
includes
machine
learning
as
a
tool
to
deliver
the
capabilities
of
the
product,
and
so
you,
if,
if
you're
gonna
value
that
product
as
an
asset,
then
you
need
to
think
about
the
appropriate
asset
management
for
the
whole
product,
rather
than
just
your
machine
learning
components.
A
So
that
really
means
thinking
about.
You
know
the
whole
of
devops
and
you're
treating
your
machine
learning
assets
as
another
piece
of
you.
C
A
So
what
we're
trying
to
do
is
get
the
tool
vendors
to
stop,
building
standalone
machine
learning,
stuff
stuff
and
start
to
integrate
their
tools
into
the
the
rest
of
the
software
supply
chain,
so
that
if
you're
gonna
build
a
product
you
can
you
can
build
it
in
one
place
with
a
consistent
tool
rather
than
having
to
have
two
completely
different
life
cycles
for
your
software
assets
and
your
machine
learning
models.
A
What
what
sort
of
problems
do
you
encounter
in
in
this
space.
B
I
think
a
lot
of
it
is
folks
coming
to
us
straight
up
asking
you
know
how
how
to
do
el
mallops,
which
is
obviously
the
big,
the
big
question,
and
wanting
expecting
expecting
kind
of
a
simple
diagram
like
they've,
seen
from
application
development
that
they
can
adapt
when
more
of
that
education
and
understanding
of
looking
at
both
their
assets
and
the
pipeline
for
maintaining
ml
assets
and
starting
from
there
and
understanding
their
goals
as
a
lot
of
the
conversations
that
you've
seen,
starting
with
a
little
less
on
the
kubeflow
and
that
those
adoption
yet
so
more
more
of
the
earlier
stage.
B
I
guess
how
does
do
ml
assets
and
exist
and
cooperate
with
existing
tooling
or
other
ci
cd
systems,
as
well
as
the
modern
argo
pieces,
so
yeah,
just
thinking
about
how
this
fits
in
more
on
their
business
process,
as
well
as
with
the
tool
chains
that
are
out
right
now
for
for
where
we're
at
so
it's
definitely
still
earlier.
But
that's
that's
often.
B
B
You
know,
take
a
iterative
approach
to
this
and
find
it
find
a
way
to
be
sensitive,
but
obviously
like
data
security
is
a
big
big
topic
that
a
lot
of
bigger
orgs
are
doing
more
things,
we're
seeing
on
on-premise,
and
that's
one
thing
that
I've
seen
less
cloud
adoption
of
the
open
tools
yet
in
terms
of
just
form
from
a
security
standpoint.
So
both
data
encryption
at
rest
and
data
security
as
well
as
asset.
You
know
longer
term
security
for
pieces
like
that.
B
That
are
often
the
bigger
taking
points
as
you've
probably
seen
and
would
hear
anywhere
else.
So.
A
A
Where
you
want,
you
know,
elastic
scalability
and
you
know
easily
deployable
atomic
asset
set.
B
Yeah-
that's
definitely.
I
can
definitely
attest
to
that
too,
and
seeing
that
approach
we're
seeing
organizations
just
look
for
that
vendor
solution
that
you
know
either
is
providing
the
models
or
providing
a
starter
point
for
them
as
jumping
into
this
being,
you
know
kind
of
overwhelmed
with
all
the
considerations
of
trying
to
construct
kind
of
your
own
mlaps
process
with
an
organization
so
yeah.
A
Cicd
for
containerized
environments-
and
it's
it's
built
around
the
sort
of
git
ops
principles,
where
everything
that
you
do
is
specified
in.
C
A
So
it
works
well
for
conventional
sort
of
build
and
deploy
of
software
assets,
and
what
we've
been
doing
is
building
up
a
library
of
of
sort
of
common
machine
learning
patterns
as
quick
start
templates
with
within
that
environment.
So
you
know,
if
you,
if
you
want
to
to
build,
you
know
a
model
in
pi
torch.
A
You
can.
You
can
pick
from
a
number
of
different
templates
that
that
solves
slightly
different
problems
in
that
space,
and
you
can
just
fire
up
an
example
project
and
it
will
build
you,
a
training
environment
for
your
model
and
a
microservice
to
wrap
the
trained
model
and
it
will
deploy
those
automatically
into
a
staging
environment.
A
C
A
You
start
with
a
working
model,
and
then
you
just
edit
your
way
from
from
that
to
where
you
want
to
be
incrementally
updating
your
git
repo
and
having
it
deploy
for
you
each
time.
B
That,
under
under
the
hood
in
terms
of
jenkins
x,
is
that
implemented
with
tecton
or
or
other
other
features
yeah.
So
so.
A
B
Yeah,
that
seems
really
really
powerful,
since
a
lot
of
the
teams
we're
seeing,
are
yeah
data
science,
heavy
and
obviously
not
interacting
with
kubernetes.
Yet,
unless
they're
coming
from
a
more
advanced
snap,
dev
background
yeah
and
I
think
I've,
I've
heard
the
similar
approaches
for
some
of
the
red
hat
tooling
that
they're
we're
working
on,
but
I'm
in
the
consulting
side.
So
I
just
I
play
with
every
product
and
work
with
whatever
I
can.
So
it's
definitely
interesting
to
hear
how
this
there's
problems
being
approached.
B
We'll
have
to
check
out
check
out
that
work.
Is
there
a
good
repo
to
see
some
of
that
poc
work,
or
is
that
out
elsewhere?.
A
So
give
me
a
week
on
that
one
jenkins
x
has
just
gone
gold
with
version
three,
which
is
quite
a
large
update.
A
And
I'm
just
updating
all
of
the
analog
stuff
to
to
be
compatible
with
that,
I'm
in
the
middle
of
that
at
the
moment,
but
it
should
be,
should
be
done
in
in
about
a
week's
time
and
and
then
it
that
will
be
very,
very
simple
to
use
it.
It's
literally
just
a
case
of
firing
up
a
jenkins
x
environment
which
is
all
done
with
terraform.
A
But
yeah
I'm
hoping
that
that
sort
of
approach
will
will
start
to
become
a
bit
more
mainstream
over
the
next
year
or
so.
A
Certainly
it
seems
to
be
the
way
that
that
google
are
heading
with
with
their
machine
learning
stuff.
So
you
know
it's
looking
like
it
will
become
more
of
a
de
facto
standard
in
the
future.
B
Yeah,
it
does
seem,
does
make
sense.
So
in
terms
of
just
your
thoughts
on
this
sig
and
the
cd
foundation
are
right
in
terms
of
working
sessions
and
what's
been
going
on,
do
you
see
more
of
the
vendor
collaboration
conversations
happening
now
in
this
stage
and
less
of
the
individual
contributors
to
projects
or
what
I
guess,
what
type
of
community
are
you
do
you
have
now
and
what
what
does
it
look
like
going
into
this
year
is
obviously
a
lot
of
these
tools
are
heading
into
closer
to
ga
in
that
sense,
but.
A
So,
to
give
you
the
history,
things
started
out
a
bit
project
centric
and
we're
now
trying
to
to
open
things
up
a
bit
and
and
make
it
more
friendly
of
an
environment
for
anybody
to
to
get
involved.
A
Because
the
reality
is
that
it's
very
very
early
days
for
and
a
lot
and
there
is
no
comprehensive
understanding
of
all
of
the
challenges
that
sit
in
that
space.
A
A
These
are
all
the
things
that
we
really
need
to
be
able
to
solve,
because
this
is
what
customers
are
doing
in
this
space
already,
and
these
are
the
daily
problems
that
they're
facing,
because
then
the
road
map
actually
starts
to
be
valuable
because
it
says
you
know
if
you
want
to
build
a
tooling
or
a
product
in
this
space.
A
Here's
the
full
scope
of
all
the
problems
that
you're
likely
to
encounter,
and
I
need
to
consider
in
in
your
solution-
or
these
are
the
challenges
that
customers
are
already
facing
and
there's
a
commercial
opportunity
out
there
for
you
to
go
and
solve
some
of
these
problems.
B
Yeah
definitely,
I
think,
the
as
kind
of
the
pillars
to
look
to
anyone.
Who's
worked
in
the
space,
I
think,
would
be
happy
with
all
what
it's
covered
and
would
understand
a
lot
of
those
problems.
So
and
is
there?
Is
there
idea
to
move
into
smaller,
cigs
or
working
groups
for
certain
topics,
or
is
that
still
in
flux.
A
Well,
right
now
we're
we're
very
flexible.
So
if,
if
we,
if
we
need
to
put
teams
together
to
do
specific
things,
then
we
can.
We
can
definitely
look
at
doing
that
as
it
stands.
A
We're
still
early
days
for
the
roadmap
and
we're
still
at
that
communication
stage
of
trying
to
get
people
aware
that
it
exists
and
you
can
use
it
and
there's
a
value
to
doing
that
and
and
then
encouraging
people
to
actually
contribute
back
and
start
extending
the
document
and
getting
involved
in
kicking
off
some
projects
to
solve
some
of
the
technical
challenges.
A
B
Also
makes
sense
seems
like
a
joint
at
the
right
time.
I
think
I
saw
the
this
event
last
year
when
things
were
winding
down
and
meetings
were
paused
for
a
while,
so
excited
to
get
on.
I
gotta
hop
to
another
call,
but
I
hope
to
yeah
try
to
get
involved
in
I'll,
be
I'm
on
the
slack
community
already,
but
try
to
make
make
these
calls.
B
Yeah
yeah
I'll
definitely
check
out
the
snacks
work
too
and
curious
to
brilliant.