►
From YouTube: CDF SIG MLOps Meeting 2020-03-26
Description
No description was provided for this meeting.
If this is YOUR meeting, an easy way to fix this is to add a description to your video, wherever mtngs.io found it (probably YouTube).
A
Okay,
okay,
so
next
week,
so
internally
I
mean
just
click
on
on
Christian
myself.
You
know
be
a
being
having
a
lot
of
discussions
internally
with
the
teams,
there's
quite
aggressively.
You
know
the
work
is
going
on
on
to
the
tech,
tour
and
cue
flow
pipeline
side.
We
have
actually
engaged
you
know
a
couple
of
Techtron
folks.
A
From
the
community
as
well
alright,
so
let's
see
you
know
if
folks
don't
join
here,
we
can
probably
you
know
unless
you
have
something
we
can.
You
know,
kill
the
call.
Let's
wait
for
a
couple
of
more
minutes
and
because
you
know
in
general
we
are
having
quite
a
lot
of
meetings
and
I'm
now
going
to
force.
You
know
a
couple
of
these
folks
from
the
Google
side
on
a
subsequent
call
right.
So
that's
there's
what
I
want
to
do.
Yeah.
B
C
B
A
A
So
I
just
ping
the
link
in
the
chat
right
so
teri
for
you
right
in
general,
and
so
all
my
running
one
of
the
things
we
do
discuss.
You
know
some
of
the
stakeholders
or
not
there.
So
there
are
two
ways
you
know
this.
This
ml
object
goes.
One
is
essentially
where
we,
you
know,
proceed
through
a
technical
charter
which
is
essentially
you
know
the
the
integration
needed
on
the
current
CI
sealy
pipelines
right
to
make
them
more
ml
also
rated,
and
the
second
is
what
30
leads,
which
is.
A
Essentially,
you
know
the
roadmap
envisioned
for
what
does
ml
ops
mean
right,
so
both
these
groups
sort
of
work
independently.
A
lot
of
the
work
happens.
You
know
oxide,
and
this
mostly
becomes
a
melting
point
of
you
know.
Getting
the
folks
together,
so
I
think
the
link
which
I
paint
was
essentially.
A
If
you
look
at
it,
so
that's
what
is
you
know
this
year
in
general
in
the
industry?
Right
have
been
this
emergence
of.
You
know
cloud
platform,
pipelines
right
and
is
is
going
to
become,
you
know
more
and
more
prominent
right.
So
obviously,
if
you
see
war,
Google
ended
up,
launching
is
the
cue
flow
pipelines
as
a
service
right.
A
The
two
key
things
to
note
in
this
is
there
you
know,
then
the
they've
taken
the
route
that
you
know.
It's
not
only
just
the
cue
flow
pipeline
SDK,
but
the
tf-x
SDK
as
well,
because
tf-x
just
on
its
own,
is
not
that
hugely
known
but
tensorflow,
as
we
all
know
is
you
know
a
gorilla
and
they
want
to
make
sure
you
know
the
end.
Users
coming
from
the
tensorflow
side
also
have
a
way
to
you
know,
run
different
components
of
tensorflow,
whether
it's
the
data
validation
model
analysis.
A
You
know
using
the
tf-x
sdk
onto
this
platform
and
over
a
period
of
time
right.
What
will
happen
is
you
know
both
the
cue
flow
pipeline
sdk
as
well
as
the
tf-x
sdk?
They
are
going
to
merge
into
one
now.
I
think
we
have
discussed
quite
a
few
times
right
that
the
pipeline's
are
taking
us
from
A
to
B,
but
from
ml
ops
perspective
right.
The
whole
notion
of.
A
Lineage
tracking
is
very
important,
so
they
have
done.
You
know
quite
a
bit
of
work
in
terms
of
you
know
ensuring
that
you
know
what
we
are
showing
here.
It's
not
generic.
You
know
CIC
D
pipeline,
but
you
know,
if
you
look
at
generating
the
graphs,
etc
four
different
steps
of
the
pipeline,
then
you
know
the
artifact
and
the
lineage
tracking.
So
if
you
can
see.
A
Let
me
kill
this
slack.
It's
just
acting
oh
yeah
yeah
and
be
able
to
trace
back
right
back.
You
know
where
what
was
the
data
set?
You
know
used
to
train
the
models.
You
know
what
was
a
tensorflow
version
without
put
of
different
components
right,
so
very,
very
rich,
lenient,
Explorer,
also
inbuilt
now
so
and
so
I
think
in
general,
you
know
there
is.
There
is
a
very
strong
adoption
for
this
in
the
community
and
obviously
for
us.
A
You
know
the
core
effort
is
to
see
you
know
if
we
can
get
to
this
stage,
but
replace
the
auto
engine
and
in
the
commas,
with
checked
on
right.
So
that's
strategically,
given
that
there
is
a
lot
of
investment
in
Techtron
from
Red
Hat
from
IBM
and
even
Google
right.
So
one
of
the
things
which
we
do
want
to
understand
is
that
checked
on
as
a
project.
You
know
oriented
from
Google's
was
initially
right
and
so
wanna
see
you
know
their
perspective
as
well
on
in
general,
the
longer
term.
C
C
Sorry,
what's
the
question,
so
we
have
like
you
know
like
in
this
yeah
it's
in
a
cloud
native
CIC
space.
We
look,
we
see
we
have
like
you
know
we
have
we've
flux
and
Argo
and
Tecton
and
a
couple
other
tools.
So
following
kind
like
they
get
ops
flow
with
our
go
and
we've
they
kind
of
combine
forces
they're
trying
to
contort
their
work
together
into
a
singular
kind
of
repository.
There's
some
ongoing
work
with
that
I
think
there's
another
sig
for
that.
A
A
C
Just
a
little
background
once
also
Omar,
so
I'm
representing
cloud
technologies
at
Apple,
so
of
Arts
in
technology
or
kind
of
like
our
chart.
Let's
look
at
multi
cloud
kind
of
strategies
for
CI
CD,
and
a
lot
of
my
work
is
also
focusing
on
the
intelligence
side
of
CI
CD.
So
I
saw
you
guys
have
this
second
hmmm
ops
and
I
was
interested
to
see
because
we're
also
we're
looking
at
mobile
cloud
for
a
lot
of
the
services
and
operations
as
well
force
other
stuff
for
doing
so
interesting.
B
We
we
feel
it's
really
important
that
we
actually
get
a
solid
grasp
on
on
on
what
ml
ops
really
needs
to
do
and
what
the
customer
demand
is
because
right
right
now,
if
you
look
at
the
way
things
are
developing
in
the
space
they're
they're
being
driven
in
a
typical
fashion
by
you
know,
we
can
do
x
and
y.
So,
let's
just
add
a
bit
of
machine
learning
to
that
and
see
what
happens,
but
things
have
not
really
been
driven
from
full-on
understanding
of
what
the
problem
space
is.
B
What
the
challenges
are
that
the
customers
are
facing
in
that
area
and
what
the
longer-term
roadmap
is
going
to
need
to
look
like,
especially
for
supporting
larger
customers.
We're
really
the
machine.
Learning
aspects
need
to
need
to
be
pushed
right
down
into
silicon,
and
you
know
where
you're
looking
to
develop
tooling,
that
can
then
get
pushed
down
into.
You
know
individual
components
that
become
very
cheap
to
deliver
and
easy
to
mass-produce.
B
So
really,
the
idea
is
very
much
an
open
collaboration
here.
We're
encouraging
everybody
to
to
get
involved,
read
and
contribute
to
the
document
start
to
to
inform
everyone
of
what
the
challenges
are
in
the
individual
spaces
that
they're
working
in
and
then
over
time.
We'll
start
to
draw
that,
together
into
some
key
pieces
of
technology,
that
we
need
to
develop
collaboratively
to
to
move
everybody
forwards
in
the
right
direction.
A
A
Is
you
know
what
you
standardize
on
is
is
also
the
one
which
becomes
the
basis
for
your
ml
ops
or
the
one
we
know
which
you
can
actually
expose
for
their
machine
learning
engineers
and
data
scientists
trying
for
machine
learning
needs,
that's
probably
the
intent
on
your
side.
Looking
at
this,
yes
yeah.
C
For
sure
that's
the
beginning
pieces
is
I
mean
from
is
identifying
kind
of
what
would
be
kind
of
a
best
practices
CD
pipeline
for
and
the
thing
is
there's
so
many
different
use
cases
right.
There's
some
machine
learning.
These
cases
there's
just
average
migrations
in
digital
transformation.
In
these
cases
that
need
to
be
done
so
a
lot
of
these
things
they
require
various
different
different
tool
sets
to
be
able
to
accomplish
them.
So
what
I'm
looking
is
looking
at
kind
of
a
future
direction?
C
It
doesn't
require
any
kind
of
monolithic
software
stack,
I'm
jabbing
at
spinnaker,
but
it's
pretty
much.
You
know
because
I
was
able
to
accomplish
CICE
through
just
stitching
containers
together
a
couple
years
back
right.
It
wasn't
something
that
was
incredibly
complex
to
kind
of
fathom,
but
given
the
nature
of
how
people
want
to
do
deployment
patterns
and
different
features
like
that,
then
you
know
you
kind
of
have
to
look
at
what
can
I
do
in
terms
of
intelligence
to
handle
these
kind
of
different
patterns
for
deployments.
C
So
the
problem
is
that
is
like
in
my
world,
I
see
that
there's
enough
actionable
intelligence
being
emitted
off
of
infrastructure
pipelines,
users
repos
that
I
could
assemble
an
intelligent
way
to
start
determining
how
how
why
and
when
I
should
be
making
deployments
into
which
cloud.
So
that's
kind
of
the
frame
of
where
I'm,
like
my
mind,
is
that
right
now
and
now,
I
can
kind
of
see
where
cube
flow
as
a
service
right.
A
A
So
there
is
a
lot
of
work
going
on
with
an
apple
right
where
your
underlying
platform
is
becoming
communities,
and
if
that
is,
is
the
route
I
think
what
your
thunking
is
is
a
right
way
if
you're
looking
at
two
things,
one,
if
you
look
at
you,
know
the
cube
native
space,
which
is
what
they
call
you
know
the
technologies
which
essentially
are
born,
because
you
know
humanities
has
become
the
ubiquitous
as
standard.
So
from
that
perspective
you
know
pipelines
like
checked
Ron,
Jenkins,
X
right.
A
They
were
essentially
catered
towards
that
right,
so
Tecton
specifically
is
essentially
as
cube
native
as
it
gets.
In
terms
of
you
know,
everything
is
architected
as
a
custom
resource
humanities,
customer
resource
using
cuban
ADC
or
DS
except
raw.
So
within
that
particular
space
Tecton
is
becoming
popular
for
the
folks.
You
know
who
are
standardizing
on
criminate.
A
In
the
mean
you
know
what
we
have
been
looking
at
technically
has
been
so
like
electron
is
you
know
for
us
sort
of
like
now
becoming
you
know
the
standardized
ICD
pipeline,
so
both
I
mean
public
cloud
DevOps
services
as
well
as
open.
If
standardized
on
Tecton
as
the
underlying
ICD
pipeline.
Now
for
the
machine
learning
pipeline,
you
made
a
decision
to
go
with
queue,
flow
pipelines
right.
A
So
there
was
a
lot
of
back-and-forth
and
discussion
between
airflow
is
your
flow
pipelines
versus
life
I
in
team
and
again
by
virtue
of
us
being
a
cube
native
company.
At
this
point
where
everything
is
running
on
top
of
humanity's,
the
decision
has
been
to
bhowmick
through
flow
pipelines.
But
what
we
want
to
do
is,
you
know,
not:
consume
cure
flow
pipelines,
as
is,
but
essentially
so.
This
is,
you
know
the
current
stage
where
cure
flow
pipeline
is
and
what
we
essentially
want
to
do.
A
Is
we
basically
flow
pipeline,
some
form
of
Tecton
right,
so
that,
essentially,
you
know
ensures
that
you
know
the
underlying
standardization
around
Tecton
is
the
CICE
pipeline
is
consumed,
reused
right.
So
that's
the
word.
One
of
the
you
know
work
which
we
are
driving
as
part
of
this
say,
guess.
Well
we're.
Essentially
you
know.
How
do
we
take
this?
So
we
have
started.
You
know,
obviously
the
first
phase,
which
we
are
almost
getting
completed,
Paul's
around.
You
know
the
compilation,
so
you
can
take.
A
You
know
the
cue
flow
pipeline
SDK,
which
is
exposed
to
the
data
scientist
and
machine
learning,
ingenious
and
compile
back
to
a
tech,
town
output
right
and
then
there
are
other
phases
so
I
think,
depending
on
from
where
you
are
coming
from,
you
know
the
first
decision
always
becomes.
There
is
not
standardization
around
see
ICD
pipeline,
that's
the
first
one
you
probably
would
want
to
make,
and
that
will
then
help
you
decide.
A
A
So
and
I
think
the
the
point
which
I
was
making
you
know
earlier
in
the
call
was
you
know
so
this
work,
which
we
have
been
doing
now.
You
know
the
goal
is
to
engage
Google
heavily
I
mean
Google
is
aware
of
it,
the
the
core
repo
is
it
to
the
cue
flow
repository
itself,
where
we
re
basing
the
whole
of
cue
flow
pipeline
to
run
off
Tecton.
But
now
you
know
we
want
to
essentially
push
it
to
the
point
where
it
becomes.
C
B
Again,
yeah
a
couple
of
quick
updates
from
from
me:
they've
been
a
few
additions
to
to
the
challenges
table
and
in
the
roadmap
I'm
just
going
through
the
review
process
on
that
and
I'll
publish
those,
hopefully
in
a
day
or
so
also
we
are
arranging
a
second
meeting
for
Asia
Pacific,
so
so
there
will
be
a
meeting
time,
which
will
be
a
few
hours
after
this
one
so
that
everyone
in
in
in
those
time
zones
will
will
be
able
to
also
collaborate
on
on
this
and
I
will
publish
those
details
shortly.
Okay,.