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From YouTube: CDF - SIG MLOps Meeting 2021-06-03
Description
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B
B
A
I'm
interested
in
trying
to
progress
things
a
bit
further
in
in
terms
of
getting
the
road
map
up
to
date
and
also
making
sure
that
we're
we're
talking
to
the
right
people
and
and
getting
the
right
requirements
in
place,
because
I
can
see
that
there's
there's
a
bit
of
a
gap
at
the
moment
between
the
work.
That's
going
on,
building
sort
of
devops,
tooling
and
ml
ops,
tooling,
versus
the
the
works
that's
going
on
in
in
building.
A
Yes,
the
supporting
infrastructure
and
platforms
to
to
actually
run
a
lot
of
the
machine
learning
work
on,
and
it
would
be
nice
if
we
could
join
some
of
those
dots
together
so
so
that
we're
getting
a
you
know
a
clearer
picture
on
both
sides
about.
You
know
what
capabilities
are
needed
out
of
the
the
pipeline
tooling,
and
you
know
what
capabilities
are
available
in
the
in
the
platforms
and
how
we
can
solve
some
of
the
shared
challenges.
B
A
A
Then
you've
got
the
the
people
who
are
building
ml,
ops
and
devops,
tooling,
and
obviously
they've
got
very
strong
cloud
backgrounds
and
lots
of
experience
in
managing
software
assets,
but
very
little
experience
in
machine
learning
and
again
very
little
exposure
to
specialized
machine
learning
hardware,
and
then
you've
got
the
hardware
providers,
who
are
obviously
right
on
the
cutting
edge
of
what's
possible
in
terms
of
accelerating
the
machine
learning,
but
also
identifying
challenges
that
come
with
trying
to
do.
Machine
learning
at
you
know
a
cluster
scale.
A
A
But
I
don't
think
we've
got
such
a
good
understanding
of
how
that
needs
to
scale
to
work.
Well
with
the
you
know,
with
the
dedicated
hardware
that's
available,
so
I
think,
there's
you
know,
there's
an
understanding
of
how
you
might
use
existing
public
cloud
infrastructure
to
to
do
this
stuff,
but
I'm
not
sure
that
we're
covering
off
all
the
bases
when
it
comes
to
you
know
using
a
dedicated
cluster
of
gpus
to
to
to
work
on
very
large
scale
problems.
B
Yeah,
I
guess
there's
the
the
training
aspect
so
sort
of
yeah
training
on
on
a
cluster
multi-node
training
and
then
there's,
I
guess,
the
inference
side
of
things
in
terms
of
hardware.
You
know
so
acceleration
of
of
the
inference.
So
I
guess
yeah
I
it
sounds
like
it's
more,
the
the
former
it's
the
disconnect
yeah
between
you
know,
training,
petabytes
of
data
on
a
huge
cluster
of
nodes,
so
so
yeah.
I
think,
maybe
what
I'm,
what
I'm
missing
is
kind
of
the
like
a
bit
of
a
background.
B
I
kind
of
came
here,
probably
not
knowing
exactly
what
we're
trying
to
achieve.
Maybe
that's
where
I'm,
where
I'm
kind
of
liking
lacking
some
knowledge.
So
are
we?
Are
there
people
sort
of
in
the
community
that
are
you
know,
working
on
or
developing
basically
solutions
to
the
problems
that
the
community
has
identified
here
or
what?
What's
the
the
goal.