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From YouTube: Machine Learning Pipelines con K3s y Argo
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A
B
Good
morning,
the
invitation
to
this
event
in
cuernavaca
in
el
salvador,
the
title
of
kings
more
in
learning
pipelines
concludes.
I
close.
It
will
be
a
small
demo
of
the
potential
that
all
this
technology
has
a
little
bit
about
me.
I
am
a
systems
engineer.
I
am
from
guatemala
taught
at
several
universities
here
in
guatemala.
C
B
Have
my
entrepreneurship
project
that
deals
with
uniting
software
communities
lemon
clausus
haití
before
it
had
other
names
right
now,
it's
called
clausus
haití
I
am
also
an
organizer
of
the
field
in
Guatemala
from
the
clown
neri
guatemala
I
am
also
involved
in
the
theme
of
the
box.
Yes,
I
decided
microservices
and
everything
related
to
this
world
that
we
are
so
passionate
about,
and
obviously
in
rulers
and.
B
B
B
So
what
we
do
is
that
we
package
the
software
and
it
allows
us
to
run
it
anywhere
once
we
have
this
container
engine
currently,,
because
there
is
a
transition
in
what
is
agreed.
That
It
is
to
be
good,
in
fact,.
The
new
version
will
no
longer
support
docker,,
but
rather
continue,,
but
in
the
case
of
docker,
at
a
low
level,
use
continue
and
to
do
this
abstraction
of
libraries
and
the
creation
part
of
the
containments,.
Then
indirectly,
we
are
too.
using
only
that
now
he
is
going
to
use
it.
B
Directly,
turbinates
is
no
longer
going
to
support
what
you
are
like
that
transition.
That
is
happening,
as
is
this
congress
about
covering
the
after
gubern.
So
let's
think
that
if
we
have
an
application
that
uses
several
sentences,
it
is
designed
under
a
microservices
architecture,
yes
or
no,
then
when
it
is
already
a
large
amount
of
content,.
Well,
we
already
use
what
is
called
an
orchestrator.
Then
what
this
orchestrator
will
allow
us
is
to
be
able
to
manage
that
amount
of
containers
and
manage
the
network.
C
B
Net
is
obviously
governed
by
a
lot
of
the
box
philosophy
and
is
well
designed
to
implement
it
or,
let's
say,
implement
processes.
I
said
and
sidi
and
well,
use
declarative
configuration
objects,
that
is,
we
have
a
file
where
we
declare,
for
example,
how
many
quantities
of
objects
we
have,.
Since
q
vernet
is
in
control
of
exactly
that.
Quantity
of
objects,
and
if
one
is
deleted,,
then
replace
that
to
have
it
in
that
state
that
we
define
or
declare
and
that
it.
B
Something
that
is
very
important
when
we
are
in
companies.
We
do
technology
concept
tests
a
script.
If
we
can
call
it,
then
these
concept
tests
allow
us
to
play
with
technology
and
determine
if
it
is,,
let's
say,
this
technology
that
we
are
testing,
is
adequate
to
solve
a
problem
that
we
are
facing,
then
to
have
this
context.
It
is
convenient
for
them,.
C
B
It
is
something
that
is
beginning
to
develop
laws
to
comply
with,,
since
it
speaks
of
the
fact
that
we
have
these
the
computation
in
the
cloud
exactly
but
close
to
the
origin
of
where
the
data
is
so,
for
example,
we
can
right
now
how
visible
this
is.
But
obviously
our
phones
are
like
computers
when
it
is
a
microcomputer
logically,
and
we
can
develop
information
processing
there,
no
longer
the
aloe,
but
in
our
local
device.
B
So
right
now
the
law
is
to
be
like
revolutionizing
and
many
of
these
microprocessor
architectures
were
being
handled
to
rm
because
they
are
cheap,,
they
have
good
performance
and
they
allow
lower
costs.
So
this
is
computing
has
to
supply
a
lot
of
the
The
need
to
reduce
processing
costs
for
certain
types
of
specific
cases
is
in
progress,.
So
there
is
a
migration
to
this
use
of
discovery
platforms
in
applications
that
we
may
have
of
next
computing,.
It
could
be
machine
learning,,
which
is
what
we
are
going
to
talk
about:
databases.
B
data
in
memory
games
and
any
type
of
load
or
processing
that
we
want
to
migrate
from
an
intel
architecture
or
x86
32
64
bits
to
an
amd
microprocessor.
So
this
is
where
one
of
the
science
projects
of
the
richter
company
well
comes
to
charge.
Well,
you
include,
well,.
It
is
a
ruler,.
It
is
light,
small,
packaged
in
a
simple
binary
that
allows
it
to
run
on
rm
architectures
and
that
it
is
already
with
that,.
B
It
already
allows
us
to
be
close
to
the
internet,
testing,
see
the
lights
confirmed,,
so
it
is
already
designed
to
run
in
production,.
So
how
You
can
see
that
the
architecture
of
rulers,
well,
it
has
a
server,.
It
has
an
agent,
the
people
who
have
packaged
everything
that
were
individual
binaries
in
one
and
in
the
part
of
the
server
also,.
Then
they
package
everything
in
a
100-
megabyte
binary,
and,
well,.
They
already
have
their
motor.
B
B
Default
is
that
it
like
it
is
it,
and,
of
course,
here
is
handling
for
more
packages.
Of
this
also
includes
an
integrated
English
controller,
which
is
graphical,
has
a
is
as
a
jamb
component
that
I
have
been
able
to
create
how
to
install
my
libraries
as
and
define
it
in
a
file
is
also
interesting.
This
integration
has
network
flan
by
default.,
I
can
put
other
gamers,
and
they
have
discontinued
as
a
container
engine
and
I
can
also
make
modifications,
for
example,
to
use
another
type
of
network,
driver,,
etc.
B
A
lot
so
it
is
something
to
observe,
as
I
told
you
that
it
instructs
by
being
a
platform
for
and
that
it
is
prepared
for
it,
and
today
they
will
be
fulfilled.
We
are
talking
about
microprocessors
that
do
not
have
as
much
power.
However,
I
cannot
use
for
normal
cases
of
deploying
a
cluster
and
the
power
to
save
costs,
either
for
a
test
for
something
in
production.
In
a
simple
end
of
easy
installation
and
maintenance
is
something
as
simple
as
I
can
create
a
cluster
using
raspberry
ce
in
my
house.
B
That
you
want
to
test
or
show
I,
don't
know
at
work,
a
preconception
of
the
option.
So
here
comes
one
of
the
problems,
maybe
not
a
problem,
but
things
to
take
into
account
to
use
this
type
of
technology
is,
for
example,.
In
a
case
of
use,,
it
is
a
woman,.
It
is
that,
for
example,.
There
are
devices
that
use
rm
that,.
Let's
say,
could
be
a
weather
station,,
something
like
that,,
so
my
applications
that
are
going
to
run
have
to
be
in
the
network.
B
B
Information
processing
and
are
beginning
to
take
this
into
account
to
migrate
their
computing
and
processing
to
the
age,
contour
invap
to
reduce
costs.
So,
although
the
is
computing
concept
is
talking
about
the
two
small
capacity
devices
doing
the
processing
where
the
data
is
there
and
not
the
new
one,
it
has
become
very
popular
that
they
say
that
there
is
already
to
be
fulfilled
in
the
cloud,
and
some
of
us
are
growing
in.
C
B
C
B
Reduced
processing
costs,
obviously
the
processing
does
it
with
lower
energy
consumption
and
allows
certain
advantages.
You
get
a
better
price.
Just
like
you
get
your
processing
or
whatever
you
are
trying
to
do
in
your
situation
in
the
company
in
the
work,
then
here
we
can
see
an
example
that
is
very
simple
as
it
instructs
after
it
is
installed.
Basically,
you
go
to
site
23.
B
Let's
say
it
recommends
a
node
of
4
gb
minimum.
Then
you
put
swipe.
In
fact,
this
is
going
to
be
placed
in
the
repository
that
we
are
going
to
in
the
end,
then,
with
this
you
can
run
all
the
installation
of
criteria
is
basically
this.
This
installation
is
like
more
customized.
Let's
say
to
be
able
to
run
harangue.
You
read
in
google
cloud
or
in
blue
with
a
node
of
at
least
4
gigabytes.
We
can
explore
with
this
with
this
command
and
it
installs.
A
B
B
And
here
we
begin
to
talk
about
the
devotee
of
this
culture.
Then
The,
one
from
box
talks
about
the
gap
that
exists
between
the
people,,
the
developers,,
the
nets,
and
the
operators,,
the
tops,
and
this
communication
that
sometimes
does
not
flow
when
doing
a
deployment
and
these
types
of
problem
situations
that
are
in
spring
production,.
There
are
errors,
and
who
it
is
to
blame
and
that
then,
the
culture
of
voice.
B
Although
it
was
born
under
a
concept
in
a
talk
in
the
usa
and
it
became
popular
and
basically
it
was
born
from
where
they
began
to
talk
with
one,
and
they
began
to
learn
one
of
us
until
it
became
one
by
one
conference,.
Then
it
is
not
like
a
methodology,,
something
like
that,,
more
like
a
culture,,
but
best
practices
have
emerged,.
People
have
written,
have
already
practiced,
and
how
this
becomes
through
the
methodology
to
phil,
well,.
Something
is
very
attractive
to
be
implemented
by
a
company.
B
B
Basically,
we
could
say,
like
the
box
applied
to
match
in
learning
only
that
there
is
different
things,
but
as
for
an
association,
we
could
see
as
a
discipline
of
this
type.
Then
the
e-mail
ops
obviously
comes
from
machine
learning,
because
the
operational
administration
in
machine
line
models
that
will
provide
a
point-to-
point:
development
of
the
entire
process.
The
process
that
machine
animal
has,,
construction,
management
of
models.
That
is
reproducible,,
that
you
can
test
the
models
and
that
the
models
can
also
evolve
to
improve
and
give
better
predictions
of
the
information
that
is
in
use,.
B
Since
here
we
do
not
automate
deployment
of
normal
software,
such
as
web
applications
and
all
that,,
but
rather
we
automate
machine
learning
models
of
the
entire
process
from
reading
data
generation
of
model
until
it
is
published
so
that
it
is
consumed
by
an
application
that
I
am
going
to
use,
then
between
the
differences
between
de
box
and
I
love
right
now,
as
if
they
say
right
now,
everything
is
what
I
want
to
observe.
So
this
is
a
machine
learning
application
so,
for
example,
differences
between
users
and
roles.
B
We
could
mention
the
bobs
best
and
developers
and
there
are
operators.
We
can
hear
that
titles
like
the
star
is
the
box
are
mentioned.
Engineers
know
that
you
like
it
is
a
culture
in
a
clown
title
engineers,
but
they
are
the
titles
that
we
find
in
the
markets
in
the
industry.
So
those
are
the
clients
and
the
users
are
the
stakeholders
that
are
involved
with
the
concept
of
opzz,
but
the
users
change
them
here.
The
users
are
data
centers,
they
are
data
engineers
or
those
of
anger
mm21.
A
B
The
technologies
because
it
is
in
the
box,
we
are
going
to
find
the
databases,
languages,
,
front-end,
vacances,
uies,
python,
mysql,
etc.
without
However,.
When
we
talk
about
e-mail
technologies,,
we
find
machine
gaming
models
and
data,.
We
are
going
to
find
payton
libraries
such
as
site,
island,
tensor
flow
and
other
types
of
software.
Libraries
for
data
processing
such
as
apache
spark
hadoop
and
cloud
services.
C
B
B
We
mentioned,
the
product
goes
early,
the
faster
we
publish,
we
reduce
errors,
already
in
production,,
a
lot
of
staff
and
trips,,
also
email,,
or
what
you
are
trying
to
do
is
automate
the
models
with
value
to
give
value
to
that
business,
that
is,
two
automate
the
inputs.
It
can
be
fast,
automating
the
process,
but
the
idea
is
to
deliver
machine,
learn
models
that
give
value
to.
B
Then
We
can
see
such
a
general
flow
of
what
the
machine
learning
part
is
and
this,
well,.
What
there
is
a
reference
is
a
great
site
where
they
can
learn,.
There
are
9
m2
in
the
references,
since
the
data
enters,,
it
generates
the
model
that
can
change
the
model,
they
compile
it
because
they
package
it
and
they
can
distribute
tasks
that
they
will
find
in
the
veil.
Because
data
ingestion
exploration.
A
B
It
goes
extraction,
transformation,
value
and
cleaning.
The
data
split,
the
data
partition,
the
and
Well,.
There
are
a
lot
of
formats
such
as
parquets,
if
it
is
vi,
a
lot
of
formats
for
information,.
Those
are
tasks
that
you
can
find
in
the
mail
ops,
you
can
find
in
the
part
of
the
machine,
learning,
Pérez,.
You
will
find
training
of
models,
evaluation
of
models,
tests
in
packaging.
Of,
the
workflows,.
We
can
mention
training
and
prediction,
and
well,.
B
Here
we
are
going
to
talk
about
the
tree,,
because
something
has
two
components,,
which
is
the
argo
workflows
and
the
city
tree,
and
well,.
That
is
in
continuous
development,,
because
basically,
what
it
offers
is
a
tool
to
do
workflows
like
this
in
general,
but
it
has
been
used
a
lot
for
what
machine
learning
is,.
It
has
become
very
popular
in
that
part,
and
it
is
an
asset
for
containers
and
obviously
for
rulers
among
the.
B
Is
that
each
step
that
you
execute
it
becomes
a
container
and
the
concept
of
graphic
graphs
directed
to
cyclics
is
handled
the
tags
for
the
famous
dax
and
it
is
like
winged
agnostic,
any
cloud
and
well,
it
is
native
for
rulers
and
with
contents.
It
is
basically
something
does
something:
workflow
is
done
for
klein
and
tree.
Yes,
It's
a
tool
to
make
play
continuums,
so
they
can
use
something
like
this
to
publish
machine
learning
models,
so
they
can,
I
think,,
because
I
stop
charts
and
all
the
things
that
support.
B
They
can't
connect
to
the
deep
hop
repository
to
keep
their
versions
of
your
listeners.
Model,
well,.
This
becomes
a
very
good
solution.
That
includes
is
more
something
citibank,
something
super
light
live
to
make
pipelines
and
deployments
of
imagined
infants.
You
are
with
a
good
match
for
m2
tools
that
could
regularly
replace.
These
cases
may
well
apply
to
a
lot
to
what
it
is
something
in
house
on
on
premise
band
in
hardware,
man
or
maybe
the
business
may
not
be
very
big,
or
they
need
to
have
that
philosophy.
Be
agnostic.
B
Not
to
be
able
to
move
from
one
place
to
another
because
they
can
come
to
replace
the
flow
that
is
in
a
well-known
tool
for
these
pipelines:
apache
lynx
that
released
solutions.
Other
types
of
solutions
that
exist
in
the
cloud,
the
benefits,
because
they
reduce
costs
for
sis
skin
and
have
a
good
capacity
for
them
to
comply
and
great
tico.
There
is
value,
locking
and
100
pesos
to
modify.
Then
in
the
demo
we
will
see
a
page.
The
pipelines
leave
workflows
how
to
process
less
in
something
workflows
will
connect
roadshows
with
rings
and
UI.
B
A
C
B
We
are
going
to
connect
to
the
machine
and
to
be
able
to
run
away
to
be
able
to
execute
it.
We
are
going
to
affect
the
following
command
when
knowing
the
worst
here
so
here,
I
have
a
bipie
line
of
hardware
flows
that
would
produce
a
command
this
at
the
same
time
as
waiting
as
an
interface
for
the
data.
There
is
the
same,
for
it
allows.
B
Through
services,
as
they
can
You
create
a
service
chest
for
your
data
before
the
company
that
has
just
the
first
step
and
is
doing
the
part
of
the
mint
display.
So
here
it
was
successfully
a
small
web
interface.
Until
we
can
see
the
result
of
the
steps,
the
output
of
the
container
well
What,
he
did
was
that
he
basically
calculated
some
student
grade
point
averages,
since
I
teach
at
universities,.
This
is
an
example
of
this,
and
well,.
He
downloads
information
from
a
bouquet
from
Google
Cloud
itself,.
B
It
transforms
me,
that
converts
the
model,
prepares
it,
and
into
the
The.
Next
step
is
the
diploma
process,,
because
what
it
does
here
is
that
it
sends
a
call
to
something
civil
and
then
it
displays
the
web
display,
and
here
we
are
going
to
do,
for
example,.
We
are
going
to
modify
a
little
here,.
We
are
going
to
make
you
that
this
is
the
code
of
tree.
B
B
So
we
can
see
in
real
time
what
happens
here
with
a
tree,
then
I
They
are
going
to
appear
here.
It
is
like
it
shows
all
the
components
of
the
deployment
of
a
service,
the
posts,
then
that
layer
goes
and
synchronizes.
It
sends
it
to
be
called
obviously
like
him,
because
it
is
wrong
or
it
is
going
to
throw
little
red
hearts
indicating
that
the
one
that
could
not
be
published
the
model
correctly.
B
C
B
C
B
B
We'll
see,
then
there
in
this
way,
well,,
they
are
already
connecting
something
with
obviously
here,
well,
each
of
each
execution.
The
pipeline
is
twenty-one
seconds
ago,
so
this
step
plus
the
long
flame
and
they
are
already
in
a
green
state.
So
our
model
is
already
there
and
well
since
it
is
basically
a
baby
beef
and
the
support
to
consume
and
I
get
the
prediction
that
given
some
notes,
that
They
are
a
partial
of
seven
points,
1
partial
27
points
and
its
average
zone
to
the
final
exam,
which
is
25
points
33.
B
B
C
B
The
piece:
how
can
I
start
learning
more
about
what
tools
recommended
books,
good
Note,
that
it
was
real
and
a
half
in
it
and
a
half,
well,?
It
has
very
good
books,.
Some.
There
are
some
books,
machine
learning,.
What
we
can
also
do
is
I'm
going
to
try,.
They
consulted
taxis
and
the
subway
to
the
media,,
we're
going
to
the
science
slack
in
El
Salvador,.
Maybe
if
it
You
can
paste
them
around,
well,
answering
there,.
There
are
many
books
on
machine.
Learning,
I
recommend
that
you
refer
to
the
likes,.
B
M
ml
heretic
point
s
is
very
simple:
ml
ops,
heretic,
point
great
site,
so
I
know
to
learn
only
about
mel
option.
I
would
tell
you
that
it
is
the
site
as
the
easiest
to
learn,
because
it
speaks
from
the
point
of
view,
as
explained
by
lemme
lops.
The
philosophy
that
it
treats
from
melo,
pge
I
would
recommend
you
start
here
and
suddenly
some
real
book
and
measure,
and
they.
C
B
B
B
Here
too,
hip
hop,
dotcom,
pl,
rm
gpl
enters
there.
They
are
in
the
ops
script
that
you
include.
If
that's
the
answer,
the
talk
shows
have
raised
right
now,
his
last
puig
with
what
the
last
part
he
says.
So
there
is
already
a
good
part
here
online
and
we
are
going
to
see
something
if
anything
and
workflows
do
you
recommend
it
for
another
workflow
apart
from
melo,
if
I
recommend
it
for
other
workflows,
it
has,
for
example,
to
do
as
trading
cards
processes.
What
happens
is
that
basically
something
workflow
is
not
only
for
machine
learning.
B
In
fact
many
mention
it
that
something
workflows
is
a.
It
is
a
generic
workflows
machine,
yes,
but
it
is
not.
It
is
not
oriented
upwards,
but
rather
it
is
a
generic
workflows
engine
a
that
they
can
use
for
anything,
but
it
has
become
very
popular
in
the
parts
of
machine
learn.
I
would
say
that
it
also
has
a
very
good
application
to
do
data
processing.
It
goes
for
what
is
data
in
the
magnet
genre,
so.
B
If
not,,
then,
suddenly
they
stick
to
us
here
and
well,
to
finish,
I'm,
going
to
say
goodbye
to
the
field,
well,,
the
resources
that
skaters
can
see
on
the
pages
of
tree
tree
for
workflows
tree
been
for
you
I
invite
you
if
you
want
to
participate
in
Claro
and
Guatemala.
These
are
the
links
of
our
myth.
It
is
we
are
a
friendly
community
of
Salvador.
Well,
we
want
to
unite
Central,
Americans
and
Latinos
lashes
likes
here.
If
you
want
to
take
trincheta
and
quick
tip.
These
are
the
other
sb
of
salvador
tree
3.
B
B
In
the
mall,
I
think
I'm
going
to
share
the
slam,
which
is
that,
if
the
case
ih
of
el
salvador
and
if
division,
el
salvador
in
the
science
flag
right
now,
making
the
buoys
work
and
who
am
I
going
to
use
the
challenges,
the
information
so
that
they
have
it
black
line.
That
already,
let
me
write
and
I'm
going
to
paste
them
right
now
in.