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From YouTube: Commit Virtual 2020: MLOps DevOps for Machine Learning
Description
Speaker: Monmayuri Ray
The practice of Devops - developing software and operationalizing the development cycle has been evolving for over a decade. Now, a new addition has joined this holistic development cycle: Machine Predictions. The emerging art and science of machine learning algorithms integrated into current operational systems is opening new possibilities for engineers, scientists, and architects in the tech world.
This presentation will take the audience on a journey in understanding the fundamentals of orchestrating machine predictions using MLOps in this ever-changing, agile world of software development. You’ll learn how to excel at the craft of DevOps for Machine Learning (ML). Monmayuri will unpack the theoretical constructs and show they apply to real-world scenarios.
Get in touch with Sales: http://bit.ly/2IygR7z
A
Hello
and
welcome
back
to
get
lab
how
to's
tips
and
tricks
here
at
get
lab
commit
2020.
Where
our
theme
is
you
belong
here,
our
next
speaker
is
monmario
re-ray
and
or,
as
we
call
her
mon
and
mon
is
also
a
gitlab
team
member
that
works
on
the
team
at
kit
lab,
but
I
actually
didn't
know
mon
before
we
started
working
on
git
lab
commit
together.
She
is
a
solution
architect
and
the
team
has
gotten
so
big.
A
We
have
over
300
employees
now
that
I
just
don't
know
all
of
the
other
gitlab
team
members,
but
when
mon
submitted
this
talk
on
ml
ops,
I
thought
this
is
really
exciting.
Here's
somebody
who's
an
expert
in
the
field
and
has
a
lot
of
knowledge
to
share
and
on
a
new
topic.
I
know
that
as
I've
worked
with
mon
and
listened
to
her,
I've
learned
a
lot
about
about
lapse
and
I'm
excited
for
you
to
learn
a
lot
too.
Let's
check
it
out.
B
B
So
for
today,
for
the
next
25
minutes
we
will
be
going
through.
Where
is
a
little
bit
of
a
history?
Where
did
this
all
start?
Why
is
this
topic
relevant
today
the
value
length
of
ai
devops
emelops,
some
basics?
What
is
a
model
and
what
then
is
emelops
and
then
the
principles
and
processes
and
simplifying
that
with
gitlab?
B
It's
a
very
interesting
story
and
I
don't
think
a
lot
of
people
actually
know
that,
but
ai
the
journey
of
ai
started
with
these
mathematicians
all
on
the
left
in
dartmouth
college
for
a
summer
program
where
they
decided
that
in
three
months
they
could
actually
solve
for
automatic
computers
and
how
can
a
computer
be
programmed
to
use
a
language,
so
that's
sort
of
where
it
all
started,
no
idea
that
that
would
actually
still
be
a
very
relevant
topic,
in
fact,
still
scratching
that
surface
of
ai.
Even
now.
B
B
The
picture
on
the
right
he's
he's
an
ai
psychologist
and
a
nobel
prize
winner,
and
and
then
there
was
actually
in
the
same
conference,
the
chief
ai
advisor
under
obama,
as
well
as
the
chief
ai
strategist
for
google
and
microsoft
and
they're
all
sort
of
coming
together
and
having
this
talk
on
what
next
for
ai
and
professor
kahneman
ends
the
conference
with
this
tale,
which
is
my
favorite
ai
tale
and
very
very
relevant
to
emma
lops.
So
the
tale
goes
like
this.
B
A
well-known
novelist
wrote
me
some
time
ago
that
he's
planning
a
novel.
The
novel
is
about
a
love
triangle
between
two
humans
and
a
robot,
and
what
he
wanted
to
know
is
how
would
the
robot
be
different
from
the
individuals
and
professor
kahneman
proposed
three
main
differences?
First,
one
obvious
robot
will
be
much
better
at
statistical
reasoning.
B
The
second
is
robot
would
have
much
higher
emotional
intelligence
very
different
to
how
we
actually
still
see
we
as
humans.
We
think
we
have
the
higher
emotional
intelligence
and
their
professor
kahneman.
He
puts
this
other
narrative
of
the
robots
having
the
high
emotional
intelligence
and
the
third
one
is
that
the
robots
would
be
wiser.
B
Wisdom
is
breath.
Wisdom
is
not
having
a
narrow
view.
That's
the
sense
of
wisdom.
It's
broad
framing
and
a
robot
will
be
endowed
with
the
broad
framing,
and
I
really
do
not
see
why,
when
it
has
learned
enough,
it
will
not
be
wiser
than
we
people,
because
we
as
humans,
don't
have
broad
framing
we're
narrow
thinking,
we're
noisy
thinkers
and
it's
very
very
easy
to
improve
upon
us.
B
So
in
one
hand
we
have,
you
know
the
futuristic
professor
kahneman
talking
about
robots
and
and
and
ai,
and
it's
it's
almost
just
everywhere.
There's
some
news:
where
there's
someone
doing
machine
learning,
siri
alexa?
All
of
that
so
then
came
this
era
of
the
economist
and
they
look
at
things
very
differently,
so
for
them
it's
just
all
about
the
value.
So
what
does
this
actually
mean
to
us
when
we
do
economist
101?
B
It's
all
about
the
demand
supply,
a
certain
price
drop
in
something
means
more
consumption
of
that
when
one
consumes
more
of
that,
it
actually
substitutes
something
else,
and
then
the
complements
of
the
more
consumption
one
is
also
gets
higher
and
people
buy
more
of
that.
So,
for
example,
when
we
say
tea,
if
there
is
a
certain
price
drop
in
tea,
people
buy
more
of
tea.
It
is
substituted
by
coffee
and
people
end
up
buying
also
more
of
sugar
and
milk,
which
is
a
compliment
of
the
tea
now.
B
B
When
we
look
at
ai,
it
is
the
drop
in
price
of
machine
prediction
where
it
is
substituted
by
human
prediction,
and
the
complement
is
data
and
human
judgment
for
devops
is
the
price
drop
in
transaction
cost
in
building
technologies
to
create
cross-functional
teams
and
a
little
bit
similar
to
area?
The
complements
is
automation
and
human
judgment.
B
B
So
giving
a
rough
understanding
of
a
little
bit
of
a
history
economics,
fundamentally,
theoretically,
what
is
mlabs
now?
We
will
first
go
to
the
basics,
now,
basics
being
what
is
a
model
we're
going
to
build
a
model
together
about
an
experiment
that
we
I
I
personally
was
part
of
this
for
a
hospital
in
sydney
where
the
experiment
goes
like
this.
We're
gonna
predict
high-risk
heart
patients
within
24
hours
of
entering
the
emergency
department.
B
Now
to
do
that
for
any
model,
you
need
data,
so
in
general,
classically
without
an
ai
model.
The
way
it
is
done
now
is
through
through
a
couple
of
rules
that
a
human
doctor
or
clinical
nurse
would
ask
the
questions
to
the
patients
based
on
three.
Four
rules
make
a
judgment
on
that
now
when
we
are
doing
ai
model,
because
a
model
can
handle
much
more
than
three
four
different
rules,
we
ended
up
taking
data
for
80
different
variables
from
historic
medicine,
understanding
to
pre-existing
conditions,
any
sort
of
test
results.
B
B
So
all
of
it
it
kind
of,
is
taken,
and
then
it
goes
into
this
cycle
where
this
is
basically
the
fundamentals
of
the
model
where
there
is
a
training
data
set
which
takes
all
these
sort
of
variables
that
we
went
through
builds
features
and
takes
the
predicted
variable,
which
is
a
classification
of
high
medium
low
of
high
risk
card.
Patients
historically
already
classified
into
a
training
data
sets
into
a
machine
learning
algorithm
into
the
model,
and
it
sort
of
goes
into
the
operations
of
running
that
algorithm
into
the
prediction.
B
So
that
is
a
model.
Basically,
is
that
input
that
leads
to
the
output
of
prediction?
This
whole
operation
is
what
is
emelop's
all
about,
so
we
looked
at
kind
of
the
features.
This
is
one
relevant
feature
which
is
actually
a
real
value
feature
of
in
ed
notes,
where
this
is
how
eating
notes
actually
comes
into
the
database,
very
noisy,
as
you
can
see,
and
and
as
you
can
see,
it's
a
human
written
with
a
lot
of
different
mistakes
in
in
an
emergency
department.
People
are
writing
really
fast.
B
So
what
we
go
through
is
actually
processing
through
natural
language
process
to
clean
up
and
take
those
relevant
features
that
we
actually
need
to
input
in
the
model.
So
the
nlp
process
is
the
relevant
features
that
goes
into
the
model,
and
then
the
model
then
detects
it
to
be
the
feature
that
is
of
the
highest
important
is
severe
tachycardia,
and
then
it
classifies
it
to
be
a
high-risk
patient.
B
B
There
is
tests
run
through
the
clinician
takes
the
notes
and
that
is
sent
to
a
database,
an
emr
database
that
is
linked
to
to
an
instance
where
the
machine
learning
model
is
built
a
lot
of
times.
It
could
be
a
data
scientist
laptop
or
a
cloud
vendor
or
wherever
that
instant
is
setting.
This
is
sort
of
the
simple
process
from
a
patient
all
the
way
to
a
data
science
just
building
the
model
to
deployment
to
production.
B
Now,
when
we
look
at
that
whole
process,
we
do
see
a
couple
of
different
parts
that
comes
out
now.
The
first
one
is
the
once
the
data
is
loaded.
The
data
scientist
has
to
do
a
lot
of
different
experiments,
a
lot
of
different
iterations
to
really
really
get
to
understand
the
data
and
build
the
features
most
of
the
time.
Each
data
scientists
have
their
own
set
of
tools
own
set
of
platforms
where
they
they
feel
comfortable,
doing
doing
the
experiments.
B
Now
that
is
fundamentally
quite
different
to
traditional
software
developments
in
when
it's
a
rule-based
java
engine,
where
it's
traditionally
just
devops
that
whole
process
of
experimentation
is
quite
quite
long.
So
that
first,
is
is,
is
one
of
the
reasons
why
emelops
or
this
whole
thing
is,
is
actually
born.
B
The
second,
the
serving
models
becomes
extremely
extremely
harder,
so
the
whole
experimentation
is
done
in
one
instant,
but
when
it's
the
model
is
served
post-production,
there
are
lots
of
differences
and
nothing
and
that
and
that
can
also
to
serve
that,
gets
much
more
complicated,
specifically
for
nlp
models,
where
we
see
there's
lots
of
clinical
notes
and
things
like
that.
Third
part
with
all
of
that
is
because
it's
all
of
it
very,
very
siloed,
laptop
to
prem
to
cloud
all
of
that.
B
There
is
no
traceability,
there's
no
orchestration
of
putting
it
together,
and
currently
we
are
in
this
very
interesting
stage
of
people
moving
from
ai
model,
proof
of
concept
to
full
production
and,
and
so
there
is,
this
need
of
deploying
more
and
more
models.
So
as
the
need
grows,
the
data
is
growing,
the
infrastructure
needs
to
grow.
At
the
same
time,
the
cohorts
of
different
sort
of
people
building
a
model
is
also
growing.
So
it's
not
just
a
data
scientist.
B
It's
the
doctors,
it's
the
clinicians,
a
whole
part
of
building
that
model,
as
well
as
software
engineers
who
actually
deploy
the
manual
even
in
data
scientists.
It's
it's
sometimes
differentiated
between
supervised
learning,
unsupervised,
learning,
nlp
models
that
was
sort
of
that
nlp
process
that
we
looked
at
so
again,
a
wide
variety
of
people
and
their
needs
are
orchestration
and
harmony.
B
This
is
actually
a
list
of
our
principles
from
a
responsible
machine
learning,
open
source
framework
which
goes
through
eight
different
principles.
Four
mlops
number
one
human
augmentation
number
three
explainability
number
four
reproducibility
and
number
seven
trust
by
privacy
is,
is
the
framework
for
those
mlabs
principles.
So
human
augmentation
goes
in
automating
a
lot
of
these
manual
tasks.
Explainability
is
having
the
logs
of
really
really
understanding
the
black
box
of
the
models
and
justifying
it.
B
Those
principles
of
leading
to
this
whole
triple
infinite
loop
of
ml
devops,
where
the
devops
part
of
the
ml
is
a
little
bit
different
to
a
traditional
devops,
where
it
actually
looks
into
also
the
algorithm
training
and
testing
apart.
The
pipelines
of
the
model,
as
well
as
the
integration
and
the
model
inference
and
model
management
part
of
it
as
well.
B
One
of
the
key
differences
of
which
hasn't
been
used.
Classically
traditionally
is
using
a
ci
service
to
be
able
to
orchestrate
through
it
so
and
the
other
part
is
being
able
to
look
into
containerizing.
Every
process
of
the
ml
pipeline
so
same
way
have.
This
shows
that
there
is
a
ci
service
with
the
source
code
container
registry,
integrated
to
the
model,
training
serving
environment
that
is
integrated
to
the
object
story.
So
every
change
in
the
source
code
triggers
the
right
part
of
the
pipeline
to
model
training
and
registers
to
the
container.
B
B
So
we
looked
into
the
ci
pipeline
and
how
we
actually
orchestrate
all
of
that
now
to
write
the
ci
ml
file
for
a
machine
learning
model
is
a
little
bit
different
to
a
traditional
java
application
or
or
any
of
the
other
non-ml
ci
ml
files.
So
it
goes
through
a
seven
steps.
This
is
a
very
generic
framework
where
it
goes
to
first
to
preparing
the
right
environment
for
the
data
scientist.
So
I'm,
for
this
part,
it's
just
a
python
environment,
but
it
could
be
r
or
it
could
be
spark.
B
B
So,
for
example,
if
there
is
one
model,
two
and
another
model
we
are
comparing
it.
These
logs
can
help
justify
why
one
model
would
actually
give
that
eighty
percent
more
it
is
more.
Eighty
percent,
accurate
versus,
maybe
the
other
being.
Ninety
percent
accurate
further,
where
it
can
be
used,
is
the
visibility
and
transparency.
B
So
the
gitlab
api
is
a
is
a
rest
api
which
helps
with
going
into
further
deep
dive
on.
Let's
say
your:
the
productive
analytics,
the
insights
dashboards
for
a
devops
life
cycle
for
the
kind
of
metrics
that
a
data
scientist
or
an
organization
would
want
to
see
is
what
kind
of
model
is
actually
working.
What
kind
of
model
takes
longer
to
train
for
a
certain
kind
of
data?
Is
it
eight
days
into
three
days
into
two
days?
B
B
The
approval
then,
once
that
is
done
in
the
experimenting
instances
is
then
goes
into
the
deployment
stage
of
redeploying
and
the
inference
application
to
the
latest
trained
version
of
the
model,
and
this
whole
thing
is
continuous
traceable
and
gives
the
gives
the
ai
applications
through
this
ml.
Ops
version
makes
that
operation
of
the
decision
making
quite
seamless
cheap,
as
well
as
automated
to
end
this
we're
going
to
go
back
a
little
bit
actually
to
the
beginning
where
we
started.
B
But
we
still
find
that
there's
a
dissonance
in
using
ai
in
in
just
our
day,
our
daily
business
decisions
or
whether
it's
because
of
the
operations,
whether
it's
because
of
it
not
getting
production
faster.
Whether
it's
data
there
is
some
sort
of
a
dissonance
to
actually
use
that
those
sort
of
applications.
B
Now,
when
we
think
about
it,
we
go
back
to
a
little
bit
of
that
original
process
that
we
saw
with
the
clinicians
and
predicting
the
high-risk
card
patients.
I'll,
probably
repeat
that
process
again,
but
so
the
way
the
process
now
is
the
patient
enters.
The
department
runs
the
test.
Clinician
takes
notes,
data
gets
sent
into
the
database,
the
notes
gets
extracted
and
the
high
risk
patients
predicted.
B
B
We
can
then
look
into
this
way.
Reverse
the
story.
Patient
enters
emergency
department
runs
through
all
the
tests.
Machine
takes
all
the
personalized
relevant
notes
where,
in
that
sl
in
the
slide
we
looked
at
the
mlp
process
already.
Does
that
clinician
then
doesn't
spend
that
time
actually
figuring
out.
Writing
those
noisy
notes
that
we
saw,
but
actually
looking
into
making
the
real
decisions
automate
decisions
through
looking
into
the
notes
that
get
sent
into
the
emr,
not
just
that
it
helps
with
the
current
strategy,
but
with
using
of
emelops,
making
the
ai
model
richer.
B
B
It
not
only
helps
with
our
current
strategy,
but
also
we
rethink
about
new
ways
and
new
ways
to
execute
those
operations
and
needs
of
humans
to
make
it
more
clear-
and
I
always
speak
about
it-
is
actually
the
amazon
recommendation
engine
so
right
now.
I
I
would
say
that
every
time
I
buy
a
shoe,
it
recommends
me
the
right
one,
twenty
thirty
percent
of
the
time.
Now
they
collect
the
data.
B
The
operations
get
better
and,
and
the
accuracy
actually
gets
to
ninety
five
percent,
and
maybe
one
day
amazon
decides
that
they
are
not
only
they're
not
going
to
wait
till
I
actually
buy
the
shoe
they're
going
to
actually
ship
me,
the
shoe
that's
theoretical,
but
that's
again
the
beauty
of
automated
decision
making
by
enabling
emalops,
and
with
that.
Thank
you
for
your
time.
I
hope
you
really
enjoy.