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From YouTube: CDF - SIG MLOps Meeting - 2021-02-25a
Description
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B
B
A
B
A
A
A
B
Yeah,
I
I
think
at
the
end
of
last
year
it
was
well
the
end
of
last
year
that
when
we
sort
of
took
a
recess,
we
were
starting
to
think
about
how
to
sort
of
get
the
word
out
there.
So
I
imagine
that
would
be
a
big
part
of
the
ames.
Here
would
be
a
more
wider
awareness.
People
are
discovering,
discovering
this
they're
discovering
that
the
road
map
and
things
and
are
very
interested
in
it,
but
yeah
there
could
be,
could
be
more
of
that.
So
I
guess
that
would
be.
A
The
other
element
is
actually
a
cdn.
The
negation
level
make
sure
that
we're
communicating
clearly
with
all
of
the
teams
who
are
working,
devops
related
science,
making
sure
that
they're
fully
engaged
and
then
considering
they're
in
the
road.
My
part
of
the
the
work
that
they're
doing
doing
on
their
own.
B
B
B
B
B
A
In
fact,
we've
now
got
a
new
sig,
which
is
specifically
looking
at
sort
of
platform
on
artifacts
and
obstructions
the
the
the
events
which
is
you
know
intended
to
try
and
line
all
of
the
the
various
tooling
again
against
a
standard
set
of
ways.
Thinking
about
managing
dev
activities
he's
in
indian
program.
A
B
So
it's
basically
it's
like,
for
example,
for
technology
making
sure
it's
possibly
suitable
for
ml
ops.
Workloads
like
it's
that
it
like
an
example,
would
be
that
it's
people
are
running
very
long
running
things
handling
things
that
deal
with
large
amounts
of
data
and
so
on.
Whatever
the
specifics
are,
is
that
the
idea
is
that
it.
B
A
So
there
are
things
that
we
can
suggest
in
those
areas
to
help
help
improve
our
abilities
to
manage
larger,
datasets
and
yeah,
where
we've
run
up
against
some
of
the
boundaries
of
what.
What
techdon
do,
because
working
with
chunks
of
data
and
or
very
very
resourcefully
means
that
they're
trying
to
work
work
with
it.
B
B
B
A
B
A
B
A
So
you
know
we've
identified
a
number
of
real
world
challenges
and
you
know
if
we
can
back
up
with
with
with
sizing
information.
In
terms
of
you
know
what
the
painting
point
that
is
really
really
costing
now
is
that
that
starts
to
paint
a
picture
of
what
the
opportunity
in
in
that
space
actually
building
building
the
solutions,
and
you
know
clearly,
people
want
to
be
working
on
on
projects
that
that
other
gonna
find
useful,
and
I
ideally
would
do
so.
B
One
other
thing
I
thought
would
be
interesting
is:
if
we're
gonna
encourage
more
people
to
be
involved
in
more
discussion,
where's
the
best
place
for
doing
it
in
text
form
or
asynchronously.
Is
it
the
slack
channel?
Someone
was
asking
whether
we
should
use
github
discussions
like
do
you
have
any
thoughts
there.
A
A
B
B
B
Yeah
and
and
we're
doing
it
monthly
at
this
time,
is
that
right
perform
for
the
next
month
or
two
yeah,
and
so
I've
got
it
as
the
well.
I
put
it
in
my
calendar,
so
it's
the
for
now
it
was
the
fourth
thursday
in
the
month
or
something
yeah
monthly.
On
the
fourth
thursday,
is
what
my
calendar.
What
google
calendar
tells
me
it's
on,
which
is
okay
with
me,
yeah.
B
B
I
think
there's
probably
some
broader
devopsey
ones
that
might
be
interesting
too.
To
that
would
draw
attention
to
it.
I
think
it
would
be
relatively
easy
sort
of
pitch
to
talk
about,
because
you
could
just
take
a
subsection
of
it
and
find
the
most
relevant
things
to
talk
about,
because
a
lot
of
it's
quite
interesting
to
people
things,
people
don't
think
about
and
the
either
the
ethical
angle
or
the
you
know
something.
B
B
In
other
areas,
is
you
know,
since
we
talked
last
there's
there
been
any
interesting
developments
sort
of
in
the
field
like
it
to
me?
There's
lots
of
interesting
applications
of
it.
There's
different
companies
have
started
and-
and
things
like
that,
but
I
haven't
seen
any
sort
of
earth
shattering
developments.
It's
just
progress
continues.
I
have
come
across
some
people
that
share
sort
of
my
opinion
that
that
some
of
us
think
it
will
be
easier
for
developers
to
learn
a
bit
of
data
science
than
data
science
scientists
to
learn
development.
B
So
it's
interesting
to
hear
some
other
people
have
that
view
sort
of
analogous
to
the
devops
thing.
There's
there's
always
a
shortage
of
data
scientists
in
the
world
and
then
not
not
that
there's
a
surplus
of
developers,
but
developers
are
usually
going
to
be
around
anywhere.
You
would
want
a
data
scientist
so
yeah.
I
think
we
might
hear
more
about
that
and
tools,
sort
of
specializing
in
one
or
the
other,
so
that'll
be
interesting
to
see.
That's
definitely
something.
I've
noticed.
A
A
Well,
well,
I
think
it's
more
that
people
are
looking
at
some
of
the
the
sort
of
you
know,
service,
movement
and-
and
you
know
the
some
of
some
of
some
of
the
activities
in
the
code
space
switcher.
You
know
you
know
intended
really
dumb
down
very
standardized
operations
so
that
it's
all
figuration
and
no
coding
right.
A
A
Yeah,
it
doesn't
look
like
things
are
improving
for
much
in
in
terms
of
the
quality
of
production
option
machining
asset
that,
if
anything,
we're
increasingly
looking
like
we're
heading
into
another
winter.
Due
to
the
the
the
number
of
failed
hell
products
out
there
that
yeah
yeah.
So
I
think
the
the
roadmap
maps
actually
going
very
important.
B
I
think
we'll
see
more
and
more
of
the
application
of
things
to
mundane
sort
of
solutions,
things
that
it's
not
machine,
learning
isn't
really
the
key
feature
of
it,
though,
but
it
just
adds
another
improvement
to
things
a
bit
like.
I
guess
when,
when
apps
started,
getting
web
app
front
ends
to
things
that
weren't
web
web
fronted
before
they
weren't
all
about
the
web
app,
but
it
was
just
one
one
piece
of
technology
in
the
mix.
B
B
B
You
know
sort
of
continuously
trained
models
based
on
you
know
daily
or
continuous
training
runs
or
weekly
so
like
when
you
have
some
legislation
that
says
something
like
an
algorithm.
Does
that
extend
to
a
model,
and
is
it
the
does?
That
then
include
all
the
data
that
the
the
private
data
that
derives?
B
The
model
and
so
on,
there's
a
lot
of
things
that
even
expert
practitioners
don't
get
the
subtleties
of
that
like
if
you've
got
a
piece
of
law
that
says,
google
has
to
tell
news
limited
when
they're
sorting
algorithm
for
daily
news
changes.
It's
like.
Well,
we
don't
know,
but
that's
likely
evolving
and
non-trivial
about
it,
machine,
learning
and
and
and
data
that
feeds
that
model.
It's
not
an
algorithm
that
someone
cooked
up
and.
A
B
B
B
But
when
it's
models
that
are
trained
from
a
significant
amount
of
data,
then
you
know
the
data
becomes
part
of
the
algorithm.
It's
not
just
static
code
that
you
can.
You
can
do
and
not
give
away
any
external
secrets
or
anything
like
that,
or
it's
just
yeah,
it's
hard
to
explain
it's,
this
sort
of
lay
understanding
of
computing
that
that's.
B
It
gets
ugly
when
it
gets
mixed
in
with
regulation,
and
it's
not
just
it's
not
just
lawyers
and
legislators,
it's
I'd,
say
90
to
95
of
developers,
wouldn't
know
that
you
know
google's
search
algorithm
or
something
like
that
is
fundamentally
machine,
learning
or
involves
machine
learning
and
and
a
bunch
of
things
they
use
every
day.
They
would
not
think
that
it
involves
it
so
yeah,
it's
it's.
If
it's
challenging
for
insiders,
then
how
can
we
expect
the
mainstream
to
be
able
to
understand
that
subtle
concept?
B
A
B
Yeah
and
it'd
be
good
it'd,
be
good
to
encourage
people
in
general
to
pop
up
in
the
slack
and
we're
all
in
different
time
zones.
So
it's
always
nice
to
see
some
asynchronous
discussion
there,
and
even
if
people
just
post,
interesting
things
that
are
going
on
in
in
their
world,
then
that's
that's
useful.
I
think
we'll
get
more
value
out
of
it.
That
way.
So
I
think
that
that
should
be
something
we
try
and
promote
this
year
is
the
slack.
I
agree:
that's
a
great
way
to
chat
it's
fairly
low.
Friction.