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From YouTube: The MLOps Roadmap - Terry Cox, Boostrap Ltd.
Description
The MLOps Roadmap - Terry Cox, Boostrap Ltd.
This talk is an introduction to the CDF MLOps Roadmap.
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So
if
you
step
back
and
take
the
bigger
view,
it
is
clear
that
jupiter
nut
notebooks
are
not
production-ready.
Assets
are
they're
very
easy
to
write
and
modify
off
the
cuff,
but
they're,
also
very
difficult
to
to
manage
effectively
put
a
good
governance
process
around
and
extremely
challenging
to
scale
and
secure,
and
all
of
the
other
non-functional
aspects
that
we
need
to
manage
when
we're
dealing
with
technology
in
production,
environments.
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So,
for
example,
because
python
is
you
know
an
interpreted
scripting
language,
it
means
that
the
the
the
source
code
of
a
python
environment
is
always
available
in
a
production
environment,
and
you
are
effectively
able
to
if
you
have
access
to
that
production,
environment,
modify
the
source
code
and
change
the
behavior
of
the
system.
That's
running
so
clearly
that
could
be
a
very
high
risk
from
a
security
perspective,
be
very
easy
to
inject
malicious
code
into
python
environments.
A
Now
the
reality
is
that
the
the
technology
that
is
most
convenient
for
writing
training
scripts
is
often
different
to
the
technology.
That's
most
efficient
for
running
the
trainings
and
again
the
technology,
which
is
optimal
for
them
operating
those
models
may
be
different
again,
so
you
know
we.
We
fully
expect
that
customers
need
to
be
able
to
easily
define
their
models
so
that
they
can.
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Which
may
imply
the
use
of
say,
gpu
resources
in
the
cloud
environment
to
give
you
short-term
access
to
a
large
amount
of
compute
resource.
But
then
the
model
you've
trained
is
is
quite
likely
to
be
some
sort
of
decision
making
system
that
needs
to
operate
in
near
real
time
in
human-facing
environments.
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And
there
are
many
ways
in
which
you
can
contribute
to
the
work
that
we're
doing
we're
more
than
happy
to
to
accept
pull
requests
on
the
document
itself.
So
if
you
feel
there's
a
there's,
a
challenge,
that's
missing
or
a
technology
requirement
that
we
should
detail,
then
you're
welcome
to
to
edit
the
draft
version
of
of
the
document
and
we'll
review
and
incorporate
any
feedback
you're.
A
Right,
I
hope
that
was
helpful
and
I'd
be
really
interested
to
know
who's
who's
involved
in
doing
ops,
related
activities
at
the
moment
and
who's
planning
to
do
so
shortly
so
feel
free
to.
Let
me
know
in
the
chat
if
you're
already
on
this
journey
and
if
you've
got
any
specific
questions
that
I
can
help
with.
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So
the
plan
really
is
to
try
and
evolve
this
incrementally
over
time.
You
know:
we've
we've
done
quite
a
bit
of
work
this
year
to
put
the
first
draft
together,
and
you
know
we.
We
hope
that
you
know.
We've
got
a
a
starter
for
10
in
terms
of
highlighting
what
a
lot
of
the
the
common
problems
are
and
some
of
the
the
bigger
challenges
you
know.
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Clearly,
what
we
really
need
to
do
here
is
to
extend
devops
so
that
it
takes
into
account
the
needs
of
of
machine
learning
and
ai
projects,
so
so
really,
the
the
the
challenge
here
james
is
to
make
sure
that
the
focus
is
always
on
reducing
lead
time.
So
what
what
the
practice
is
doing
is
setting
you
up
to
minimize
the
time
it
takes
you
to
get
from
a
decision
about
a
feature
to
implementing
that
feature
in
a
production
environment.
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So
you
know
there
are
some
significant
gaps
in
our
current
tooling
and
capability
in
terms
of
being
able
to
treat
a
large
set
of
data
as
a
coherent
asset
that
we
we
need
to
manage
over
time,
and
you
know
to
give
you
a
feel
for
that.
A
large
data
set
in
machine
learning
terms
probably
starts
at
about
10
terabytes
of
data
and
may
go
up
to
tens
of
petabytes.
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They
need
to
be
surrounding
you
in
your
smart
cities
and
in
in
many
cases
they
need
to
operate
very
low
latency.
So
you
you
need
the
system
to
react
to
what's
going
on
and
and
respond
in
milliseconds,
because
you
may
be
dealing
with
a
safety
related
system
like
an
automated
braking
system.
On
a
vehicle
or
a
network
of
systems,
that's
interpreting
traffic
and
trying
to
optimize
the
operation
of
a
set
of
traffic
lights.
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So
an
individual
gpu
card
might
have
you
know,
40
gig
of
ram
available
so
to
to
be
able
to
train
a
model
in
a
reasonable
time
scale.
You
know
less
than
a
week
you,
you
typically
need
to
have
a
lot
of
gpus
and
then
a
very
high
speed
network
so
that
you
can
continuously
stream
the
data
in
40
gig
chunks
through
the
network
performing
a
lot
of
calculations
on
it
and
then
synthesizing
a
model
on
the
back
of
that.
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So
serverless
is
an
abstraction
that
we
can
use
at
one
level.
So
from
a
data
scientist
perspective
you
can,
you
can
say,
yeah
well.
This
looks
like
it's
serverless
in
that
I
don't
have
to
think
too
much
about
the
infrastructure.
I
just
tell
it
what
I
want
it
to
do,
but
under
the
hood,
the
the
mlops
implementations
actually
need
to
physically
allocate
gpu
hardware
to
nodes,
and
then
you
know,
individual
containers
have
to
mount
individual
gpu
instances
and
we
have
to
help
to
distribute
the
workload
across
those.