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From YouTube: CDF - SIG MLOps Meeting 2021-08-12 (2 of 2)
Description
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A
B
Right
I've
been
interested
in
mlaps
for
some
time.
I
guess
this
year
I
have
some
background
machine
learning
and
that
kind
of
part,
but
now
I'm
moving
towards
the
cloud
and
much
more
developer
operations,
kind
of
side
of
things
and
that's
how
I
kind
of
found
out
about
the
cd
foundation
and
before
this
I
spent
some
time
looking
at
what
emulates
first,
I
think
there
was
a
conference
over
a
cdcon
and
there
was
a
presentation
over
as
well,
so
the
current
consumer
captured
my
interest
and
I've
been
thinking
like
as
a
developer.
B
I
would
like
to
be
so
sufficient
to
be
able
to
make
some
products
on
my
own
like
projects
right
and
in
corporate
machining
into
them.
So
that's
why
I
really
wanted
to
understand
how
machine
learning
operations
kind
of
work
together,
decelerating
all
technologies
out
there
like
what's
practices.
What
problems
do
you
guys
have?
B
So
that's
kind
of
my
like,
where
my
interest
comes
from
and
that's
kind
of
what
I'm
trying
to
look
ahead
and
learn
from
the
sick
here,
just
trying
to
like
understand
what
kind
of
problems
they
are
and
then,
and
if
there
is
some
way,
how
can
I
help
to
contribute
to
that
to
be
like?
If
I
can
share
my
knowledge,
you
if
I
can
help
with
my
time,
they're
pretty
good
as
well.
B
A
A
B
So
I
I
have
a
question
about
envelopes
right.
How
is
this
different
from
data
engineering,
virtual
machine
learning
right?
I'm
expecting
that
you
have
a
component
for
machine
learning.
That's
somewhere
between
the
pipeline
that
you
have
with
data
engineering
like
you
have
some
ingress.
You
have
some
egress,
you
have
a
pipeline
and
then
you're
probably
doing
some
predictions
for
the
machining
component.
Is
that
right.
A
So,
to
give
you
a
little
bit
of
background
a
lot
of
what
goes
on
in
the
machine
learning
space
at
the
moment
has
come
from
the
data
science
field
rather
than
from
the
sort
of
software
engineering
field,
so
in
in
that
space
you're.
Typically
looking
at
a
group
of
people
who
come
from
a
mathematical
background
and
studied.
B
B
B
A
A
B
B
A
B
A
B
It's
not
anything.
Some
pilot
process
is
a
bit
like
trying
to
understand
what
data
was
used
to
create
a
kind
of
model
as
well
and
trying
to
make
sense
right.
Is
there
any
difference
in
data
that's
coming
in
the
future,
as
in
data
versioning
right
that
we
have
some
systems
over
there
like
we
have
where
source
code
versioning
would
get
so
your
bit
bucket
do
stuff
like
that.
So
I
think
that's
also
a
component
of
it,
because
I've
seen
people
treat
machine
learning
models
as
black
boxes,
where
you
just
given
some
data.
B
It
gives
you
some
output
and,
most
of
the
time
I've
seen
innovation
come
from
people
learn
to
do
feature
engineering
properly,
where
they
manage
to
extract
the
right
properties
and
the
features
from
a
given
data
set.
I
think
that's
where
the
most
value
I
think
comes
from
when
it
comes
to
deploying
models
and
testing
out
the
accuracy.
Obviously,
every
organization
would
want.
I
don't
want
to
do
the
best,
but
there's
100
machine
learning.
It's
never
really
possible.
A
Yeah
you're
absolutely
right.
There's
there
are
some
very
significant
challenges
and
in
fact
the
data
versioning
one
is
very
easy
to
say,
but
to
actually
implement.
It
is
incredibly
difficult
because,
if
you've,
if
you've
got
say,
10
petabytes
of
training
data,
then
the
practical
challenges
of
managing
versions.
Snapshots
of
that
much
data,
especially
if
it's
changing
significantly
on
a
on
a
daily
or
weekly
cadence.
B
Right,
so
so
what
so,
so?
What
does
eminent
always
propose
as
a
solution
right?
There
are
different
solutions,
some
like
coming
from,
because
I
also
have
an
interest
in
software
architecture
and
database
design
stuff
like
that
and
coming
from
that
perspective,
I
think
there's
always
a
trade-off
when
you
have
to
make
when
you're
working
with
such
a
data
set,
you
have
to
think
about
backups.
You
have
to
think
about
versioning,
you
think
about
if
everything
is
being
consistent
and
there's
also
property
of
assets,
if
you're
doing
such
kind
of
property.
B
If,
for
example,
you
have
an
application
that
has
some
kind
of
streaming
kind
of
machine
learning
where
you
say
like
facebook
right,
facebook
has
a
lot
of
different
mechanisms
built
into
it
and
some
functionalities
or
features
try
to
guess.
You
recommend
you
something
based
on
what
kind
of
drinks
like
things
are
you
liking
your
posting
or
stuff?
Like
that,
I
mean
I
should
recommend
you
the
same
kind
of
things
in
the
future
as
well.
B
So
there's
also
the
idea
where
you
have
to
continuously
get
data
from
the
user,
and
then
you
have
to
process
the
output
or
somewhere,
and
then
you
have
to
make
it
a
pipeline
in
such
a
way
right
where
you
had
a
machine
learning
model
and
learning
is
from
you
know,
new
data
that
is
all
coming
into
it
at
the
same
time
and
it
has
to
give
some
predictions
on
this
of
that
problem.
So
it's
a
very
changing
kind
of
model
that
you
have
to
think
about
as
well.
A
B
So
I
can
see
people
from
software
engineering
coming
in
and
learning
about
how
to
deploy.
Machine
learning
model
is
exactly
possible
because
I
think
businesses
will
want
to
put
features
that
can
they
like
they
can
get
from
machine
learning
into
their
products
as
soon
as
possible,
and
for
the
principal
of
agile
that
we
have
weaver,
obviously
likes
a
solution
that
would
remove
the
barriers
and
the
hardness
that
it
is
to
make
a
machining
model.
B
This
doesn't
have
to
be
the
soda
right,
the
standard,
the
state-of-the-art
kind
of
model
that
we
talked
about
in
research,
your
kind
of
your
academics,
but
it
can
be
a
simple
model
which,
for
example,
has
access
to
seven
percent
accuracy.
I
think
most
businesses
will
be
okay
with
that
and
they
would
like
software
engineers
to
come
in
and
and
then
help
them
easily
deploy
and
manage
this
kind
of
machine
learning
models
in
their
applications.
B
So
they
can
provide
some
kind
of
systems
right,
because
you
often
have
I've
like
been
through
some
machine
learning
courses,
and
we
often
have
the
examples
of
recommendation
systems
where
you
can
recommend
a
user
something.
But
what
bothered
me
about?
Those
was
that
when
I
was
sitting
down,
I
shoot
and
implement
kind
of
that
kind
of
thing
in
jupyter
notebooks.
I
realized
I
was
given
a
data
that
was
already
collected
from
somewhere
and
while
I
could
give
some
protection
like
I,
I
can
give
you
some
accuracy
or
position
over
there.
B
B
A
Now,
what's
what's
supported
in
in
the
main
cloud
environments
is
based
on
the
demands
from
customers
for
particular
types
of
solution.
So
it's
a
bit
of
a
circular
situation.
Right
now
in
the
the
cloud
environments
are
providing
jupiter
notebooks
in
production,
because
that's
what
the
customers
are
asking
for,
and
so
there's
there's
been
a
drift
towards
that
particular
way
of
working,
but
there
hasn't
been
sufficient
work
to
evaluate
whether
that
was
the
right
direction
or
whether
it's
going
to
lead
to
escalating
problems
in
the
future.
A
So
they've
got
minimal
practical
experience
and
they're
they're
just
trying
to
do
whatever
is
easiest
to
run
an
experiment
and
and
try
and
incorporate
it
into
an
application
yeah.
So
that
means
that
most
people
are
at
the
at
the
bottom
of
the
ladder.
In
terms
of
the
journey
to
having
a
machine
learning
application
in
production
at
scale
for
an
extended
period
of
time,.
B
A
A
So
what
there's
there's
a
there's?
A
large
number
of
people
trying
to
enter
at
the
bottom
of
the
ladder
and
statistically
about
80
percent
of
them,
are
failing
to
make
it
into
production,
because
the
the
bits
that
they're
doing
only
get
them
so
far,
and
then
they
run
into
a
show
stopper
problem
and
they
they
can't
go,
live.
B
A
B
B
A
B
Thank
you.
That's
that's.
Awesome
yeah,
like
I'm
super,
excited
to
be
like
a
part
of
something
like
this,
because
I
feel
like
some
like
envelopes
is
something
that
I
think
will
blow
up
in
the
future
as
machine
learning
has
done
before,
but
instead
of
like
the
r
and
d
part
of
it
we're
moving
towards
the
production
kind
of
part
of
it
right
and
how
we
can
quickly
appropriate
machine
learning,
changes
in
the
model
and
stuff
like
that.
B
So
I
think
mlaps
is
the
right
kind
of
perspective
on
that
kind
of
thing
and
there's
something
that
I
can
unlock.
I
actually
want
to
work
professionally
sometime,
if
I
can,
as
a
machine
learning
engineer.
So
I
guess
a
bit
more
background
on
me
was
with
them.
I
started
learning
on
machine
learning
back
in
last
year's
june
and
july
much
more
seriously.
I
got
a
certification
and
and
indeed
running
from
deep
learning.ai.
B
But
after
that
I
felt
like
I
shall
think
about
what
are
some
ideas
or
points
that
I
can
make
that
have
machine
learning
in
them
right
and
I
was
quickly
dumb
struck
because
I
could
not
find
any
easy
way
of
putting
my
models
out
there
or
I
was
very
limited
to
what
technology
I
was
using.
So
I
have
like.
I
only
knew
one
way
of
doing
this
in
this
kind
of
thing,
but
I
did
not
even
know
if
this
came
in
this
other
technology
right,
because
I
could
not
find
a
drill
on
it.