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From YouTube: OpenShift Commons Briefing #100: Adaptive Threat Protection for Large-Scale OpenShift Environments
Description
The OpenShift platform is enabling enterprise organizations to take advantage of container technologies such as Docker and Kubernetes to build, deploy, and run applications with unprecedented agility, scale, and speed. But containers also create highly dynamic, distributed, fast-moving attack surfaces that create new challenges for security teams. In this briefing, Wei Lien Dang, VP of Product at StackRox, covers the following topics:
– How containers and microservices change the threat landscape
– Examples of container attack vectors
– Best practices for monitoring, detecting, and mitigating threats to containers
– How StackRox complements security measures available in OpenShift
– How Global 2000 enterprises are using OpenShift and StackRox together
A
Well,
hello
and
welcome
again
to
another
OpenShift
Commons
gathering
this
time
we
have
a
new
open
ship.
Commons
member
stack
rocks,
that's
going
to
introduce
themselves
and
there
they
do,
and
what
they're
working
on
around
security
and
open
shift
and
you're
really
pleased
to
have
Wei
Lin
Dan
with
us
who's
the
VP
of
product
from
staff
rocks
as
well
as
a
couple
other
members
of
the
staff
rock
scene
in
waiting
in
the
chat
room.
As
always.
What
we're
going
to
do?
Is
that
really
give
his
presentation?
A
You
can
ask
questions
in
the
chat,
we'll,
try
and
answer
them
and
we'll
have
some
live
Q&A
on
the
end
of
this
on
also
the
recording
and
the
slides
will
be
posted
up
at
the
Plumbing's
blog
or
you
go
make
that
make
make
the
slides
and
the
resources
available
from
blog
that
open
ship
comm.
So
whaling
take
it
away
great.
B
B
Adoption
of
container
platforms
like
open
shift
next
I'll
provide
an
overview
of
how
stack
rocks
solves
critical
security
challenges
that
arise
when
using
containers
and
after
that,
I'll
walk
through
some
examples
of
threat,
vectors
and
use
cases
that
sack
rocks
helps
enterprise
customers
with
today,
including
some
of
the
attacks
we've
seen
in
those
customer
environments
and
then
I'll
show
a
live
demo
of
the
product
running
on
the
open
shift
platform.
I'm
happy
to
take
questions
along
the
way.
So
please
feel
free
to.
B
Let
me
know
if
there
are
any
I'll
start
with
a
brief
background
on
stack,
rocks,
we're
a
security
company
that
focuses
on
protecting
enterprise
container
environments
and
we
help
enterprises
accelerate
their
ability
to
benefit
from
the
agility,
speed
and
scale
that
containers
provide.
Our
customers
are
some
of
the
world's
largest
companies.
Many
who
have
selected
OpenShift
as
their
preferred
container
platform
and
many
who
are
already
running
containers
in
production
today.
B
These
companies
spend
a
variety
of
industries
and
they
all
use
stack,
rocks
for
a
comprehensive
detection
prevention
and
response
for
their
containerized
applications
now
stack
rocks.
Does
this
with
the
industry's
only
security
platform
that
delivers
adapted
threat
protection
and
the
adaptive
attribute
of
our
platform
is
a
reference
to
the
adaptive
security
framework
developed
by
Gartner
but,
more
importantly,
it's
also
a
key
aspect
of
our
platform
being
based
on
several
technical
breakthroughs
and
those
breakthroughs
span,
instrumentation
analytics
and
machine
learning
and
automating
all
of
those
functions
across
the
entire
security
lifecycle.
B
B
B
B
So,
given
the
dynamic,
fast-moving
attack
surface
that
containers
result
in
conventional
detection,
techniques
will
need
to
be
rethought,
the
ones
that
rely
on
signatures
and
heuristics
no
longer
work
and,
at
the
same
time,
these
new
application
architectures
are
relatively
new
still
and
they're
still
quickly
evolving.
So
the
entire
range
of
threat,
vectors
that
is
going
to
emerge
is
really
still
unknown.
B
Existing
enterprise
security
infrastructure
was
not
built
to
address
these
types
of
challenges.
If
you
consider
traditional
security
tools,
they
don't
have
the
visibility
or
awareness
into
what
containers
are
doing.
They
instead
rely
on
approaches
like
IP
based
rules
that
are
less
relevant
or
they
utilize
architectures
that
don't
extend
to
the
container
level
and
the
amount
the
container
activity
in
these
large-scale
environments
can
also
overwhelm
the
typical
security
workflows
that
we're
never
designed
to
actually
analyze
data
at
the
speed
volume
generated
by
thousands
or
tens
of
thousands
of
containers.
B
Since
containers
provide
a
new
layer
of
abstraction
in
the
stack
and
this,
you
know
it
actually
highlights
the
failure
points
of
traditional
security
in
these
new
environments,
because
with
containers,
the
focus
for
security
ends
up
needing
to
shift
away
from
tools
that
are
tied
to
specific
parts
of
the
stacks,
such
as
the
network,
the
host
or
storage
and
in
containerized
environments.
Security
instead
needs
to
operate
differently.
B
It
needs
to
better
match
the
application
components
it's
intended
to
protect,
whether
those
are
containers
or
micro-services,
architectures
or
so
on
is
the
only
way
you
can
actually
keep
up
with
applications,
while
remaining
transparent
to
what
DevOps
teams
are
trying
to
do.
What
this
means
is
that
security
needs
to
become
more
workload.
Centric.
If
you
look
at
the
evolution
of
the
stack,
security
needs
to
be
applied
continuously
and
consistently
as
part
of
the
connective
tissue,
so
to
speak,
that
glues,
distributed
applications
together.
B
If
you
are,
if
you
look
at
the
openshift
platform
itself,
it
provides
several
security
capabilities
that
enable
you
to
securely
build
images
and
deploy
containers
from
those
images
in
the
build
phase.
Best
practices
require
ensuring
that
your
container
images
are
scanned
for
known
vulnerabilities.
That
trust
is
established
over
the
content
of
those
images
and
that
the
ability
to
pull
and
put
push
and
pull
images
to
and
from
the
container
registry
and
to
access
the
container
cluster
are
appropriately
limited.
So
OpenShift
provides
a
set
of
capabilities
that
helps
customers
with
this.
It
provides
vulnerability
scanning.
B
The
atomic
scan
allows
you
to
sign
images
with
GPG
keys.
It
also
incorporates
robust
role
based
access
controls
and
Red.
Hat
has
actually
been
a
leader
in
pushing
to
get
those
capabilities
integrated
into
kubernetes
itself
and
in
the
deploy
phase.
You
have
to
think
about
hardening
and
isolation
of
containers
throughout
the
cluster
to
appropriately
limit
upfront.
What
they're
supposed
to
be
able
to
do,
and
also
to
ensure
that
sensitive
data
is
not
exposed
in
the
process
now
OpenShift
leverages.
B
The
secrets
management
that
is
built
into
kubernetes
and
support
was
actually
recently
added
and
announced
to
encrypt
those
secrets
at
rest
in
at
CD
kernel
level.
Security,
on
the
other
hand,
can
also
be
applied
using
mechanisms
like
SELinux,
you
know,
which
is
very,
very
commonly
used
on
rail
systems
as
well
as
set
comp
profiles
and
projects
and
network
policies
within
OpenShift
can
also
provide
additional
isolation
between
containers.
B
How
stack
box
complements
the
security
provided
in
openshift
is
that
we
primarily
focus
on
security
during
the
runtime
phase,
which
is
when
containers
are
actually
running
at
scale
and
dynamically
coming
and
going
all
the
time.
This
encompasses
capabilities
such
as
monitoring
and
network
visibility,
inclusion
detection,
exploit
prevention,
data
protection
and
instant
response
and
forensics.
B
The
ability
to
triage,
investigate
and
understand
the
root
causes
of
security
incidents
on
their
running
containers,
how
our
product
actually
achieves.
This
starts
with
what
we
think
of
as
a
container
native
architecture
stack
rocks,
is
made
up
of
a
set
of
integrated
security
containers
that
deploy
and
run
alongside
your
containerized
applications.
B
This
allows
stack,
rocks
to
easily
fit
in
with
and
effectively
become
built
into.
Your
container
stack
it's
ignite
in
more
specifically,
it's
agnostic
and
abstracted
from
the
underlying
infrastructure
and
any
other
container
tooling
that
you
selected
we
integrate
with
all
the
lien
container
platforms,
including
openshift,
and
as
an
example
of
how
we're
actually
agnostic
and
abstracted
from
that
underlying
infrastructure.
We
have
customers
who
have
chosen
to
deploy
OpenShift,
both
on-premises
in
their
data
centers,
as
well
as
on
multiple
public
clouds.
B
We
run
a
container
on
each
host
in
your
cluster
to
collect
signals
about
container
activity
and
how
we
approach
detection
is
threat.
Detection
is
this.
Data
is
then
evaluated
for
various
indicators
of
attacking
compromised
and
analyzed
using
machine
learning
based
modeling
from
the
data
we
see,
we
build
up
a
deep
understanding
of
application,
behaviors
and
attacker
techniques,
and
these
are
all
correlated
across
the
environment
to
then
identify
attacks,
policy
violations
and
other
types
of
threats,
I'd
like
to
spend
some
time
to
elaborate
on
what
this
means
for
openshift
environments.
B
More
specifically,
our
platform
is
distributed
as
a
set
of
container
images
that
are
used
to
launch
the
stack
Rock
services
into
your
openshift
cluster,
but
rather
than
just
providing
openshift
users
with
a
set
of
docker
images,
we
build
innate
integrations
with
the
platform
itself
and
this
integration.
This
level
of
integration
means
that
bringing
up
stack
rocks
in
openshift
is
very
easy.
You
can
utilize
your
existing
Orchestrator
tool
chain,
including
the
open
shift,
CLI
familiar
OC
command,
syntax
and
yamo
files
to
launch
and
manage
stack
rocks.
B
B
These
are
some
of
our
data
analysis
and
machine
learning
services.
These
can
run
on
nodes
that
are
separate
from
where
your
containerized
applications
are
actually
running,
and
in
this
operational
model,
security
can
start
to
be
treated
as
and
considered
as
just
another
application
within
your
cluster.
It
runs
alongside
your
containerized
applications.
It's
managed
the
same
way
and
operationalize
the
same
way
in
terms
of
deployments
and
upgrades
back.
B
Additionally,
stack
rocks
is
able
to
auto
discover
a
containerized,
openshift
and
kubernetes
services.
We
monitor
and
detect
threats
on
the
containers
that
are
actually
running
OpenShift
services
themselves,
and
this
is
very
interesting
because
the
orchestrator
were
represents
a
distinct
and
separate
attack
surface
that
is
also
critical
to
safeguard.
B
What
makes
stack
rocks
is
approach,
so
unique
is
that
it's
built
on
breakthroughs
in
three
key
technical
areas
that
rocks
itself
runs
as
a
set
of
security
micro
services,
and
this
allows
us
to
achieve
orders
of
magnitude,
better
automation,
dáil
and
speed
than
traditional
security
tools,
and
today
stack
rocks
as
an
example.
Staff
today,
stack
rocks
can
detect
and
stop
a
web-based
attack
with
data
exfiltration
across
a
set
of
distributed
containers
in
a
matter
of
seconds.
B
The
suspicious
network,
communications,
unusual,
container,
commands
or
privilege
escalation
users
also
have
the
option
to
configure
automated
enforcement
actions
that
can
be
taken
based
on
what
stack
rocks
detects
and
these
enforcement
actions
can
either
be
preventive
in
nature,
such
as
blocking
container
commands
or
system
calls
or
responsive,
which
include
things
like
pausing
or
quarantine
in
containers
for
further
investigation
and
what
those
capabilities
allow,
for
instance,
are
it
gives
security
teams
a
chance
to
actually
evaluate
and
analyze?
What's
going
on
within
a
running
container
in
an
isolated
setting
before
the
container
actually
goes
away?
B
Otherwise,
the
information
is
lost
once
the
container
gets,
killed
or
gets
torn
down,
and
this
stack
rocks
approach
is
designed
to
defend
against
a
variety
of
threat.
Vectors
we've
learned
very
important
lessons
from
the
prevalence
of
cyber
security
attacks
and
data
breaches
over
the
last
few
years,
mainly
that
hardening
and
prevention
are
by
themselves
not
sufficient
security
teams
need
detection
and
response
capabilities
that
enable
them
to
mitigate
the
impact
of
security
incidents
and
to
be
able
to
actually
respond
to
them.
B
On
the
next
couple,
slides,
including
this
one
I'll
highlight
some
attack
patterns
that
enterprises
need
to
be
ready
for,
in
this
first
case,
that's
highlighted
on
this
slide.
This
was
an
attack
pattern
brought
to
our
attention
by
a
large
Bank,
and
the
attack
vector
consists
of
multiple
phases
that
start
with
code
injection
and
result
in
data
exfiltration.
B
If
you
consider
the
use
cases
for
containers,
many
organizations
are
using
containers
to
build
a
highly
scalable
web
tier,
and
these
containers
are
susceptible
to
a
variety
of
different
web-based
attacks.
Indicators
that
stack
rocks
would
look
for
in
this
scenario,
include
malformed
content
or
suspicious
or
known
malicious
incoming
IP
address.
B
Now
after
comprising
the
webapp
container,
the
attacker
starts
to
move
laterally
east-west
within
the
environment,
and
this
can
be
identified
based
on
anomalous,
Network
communication
or
the
use
of
non-standard
ports
once
the
attacker
lands
on
a
container.
That
is
part
of
an
internal
brokerage
app
service,
and
this
is
a
service
that
the
webapp
service
talks
to.
He
starts
to
establish
persistence,
basically
embedding
himself
into
the
environment
and
indicators
that
can
be
attributed
to
this.
Behavior
include
privilege,
escalation
or
since
the
file
system,
access
or
changes.
B
The
attacker
then
moves
laterally
to
the
DB
service,
and
the
DB
service
in
this
case
is
likely
ultimately
the
intended
target
of
this
attack
vector
now.
If
the
attacker
starts
to
exfiltrate
data
from
that
DB
service,
then
stack
rocks.
The
text
is
based
on
indicators
such
as
a
substantial
HTTP,
payload
or
suspicious
outgoing
IP,
which
may
represent
command
and
control
communication,
or
something
similar
now
stack
rocks
first
automatically.
B
If
you
look
across
these
various
vectors
that
have
happened
as
part
of
this
attack,
pattern,
sack
rocks
first
automatically
surfaces
the
different
types
of
indicators
that
span
these
different
phases,
but
by
themselves.
Each
of
these
indicators
do
not
actually
surface
that
a
much
larger
problem
is
at
hand,
and
so
what
stack
rocks
uniquely
does
in
this
type
of
scenario,
is
we're
able
to
detect
this
type
of
serious
attack
pattern
by
understanding
how
these
indicators
are
actually
related
to
each
other
across
containers
within
the
distributed
environment?
B
On
this
next
slide.
Another
attack
pattern
that
we
worked
to
address
in
this
case
with
a
US
government
organization,
is
one
that
targets
secrets
and
again
going
from
left
to
right.
It
begins
with
a
vulnerability
exploit.
In
most
cases,
we
have
we've
observed
that,
even
if
the
container
platform
itself
has
been
hardened,
containers
that
are
running
still
have
vulnerabilities,
new
ones
are
being
discovered.
All
the
time
and
indicators
of
this
type
of
exploit
may
include
an
attacker
launching
anomalous
processes
or
communication
originating
from
a
particular
IP.
B
The
attacker
then
establishes
a
reverse
shell
on
the
webapp
service,
and
this
could
be
discovered
by
stack
rocks
based
on
indicators
such
as
anomalous,
outgoing
communication
or
usage
of
non-standard
ports.
Now,
after
that,
the
attacker
accesses
the
filesystem,
and
these
were
see.
These
are
aware.
This
is
where
secrets
may
be
temporarily
stored
and
may
be
exposed,
and
so
the
attacker
extracts
those
secrets
in
order
to
detect
that
we
would
look
for
indicators
such
as
file,
system
access
or
sensitive
data
exposure,
and
the
last
step
involves
the
attacker
initiating
a
remote
procedure.
B
Call
that
causes
the
container
to
either
exceed
its
resource
constraints
or
run
a
process
that
results
in
the
container
getting
killed
or
something
similar,
and
in
this
case
the
attacker
is
actually
seeking
to
get
rid
of
all
traces
of
his
activity.
This
is
the
equivalent
of
the
crime
scene
being
wiped
before
the
detective
arrives.
B
Before
I
switch
over
to
the
demo,
I
want
to
highlight
some
key
use:
cases
that
open
ship
users
look
to
stack
rocks
to
address.
The
first
is
container
discovery
after
deploying
stack
rocks.
Many
customers
are
often
surprised
to
find
just
how
many
containers
are
actually
running
in
their
environment.
That
did
that
they
didn't
know
about,
and
so
stack
rocks
makes
auditability
behavior
profiling
in
search
base
discovery
very
easy.
B
We
also
have
customers
who
are
deploying
on
premises
in
the
public
cloud
or
moving
containers
across
both
and
want
security
to
easily
span
these
types
of
hybrid
environments
and
so
they're.
Looking
for
what
we
call
cloud
workload,
protection
zach-bots
provides
the
security,
that's
required
to
scale
with
infrastructure,
particularly
cloud
infrastructure
that
is
highly
dynamic
as
a
result
of
being
provisioned
on-demand,
and
a
third
use
case
is
tied
to
the
fact
that
sac
Rock
service
is
very
detailed
event
context.
B
What
you're
currently
seeing
here
is
a
view
of
the
stack
Rock's
portal,
which
provides
a
web-based
user
interface
and
runs
as
a
separate
micro
service
right
now,
it's
running
in
a
container
on
OpenShift
and
what
I've
done
ahead
of
time
is
set
up
an
open
shift,
cluster
and
deployed
stacked
rocks
along
with
several
example.
Applications.
B
This
cluster
is
live.
You
can
see
the
current
time
in
the
upper
right,
and
this
is
a
10
node
cluster.
That's
running
86
containers
sack
box
can
be
deployed
using
your
existing
open
shift
tool
chain
and
once
that's
done,
the
machine
learning
models
are
automatically
built.
Based
on
the
signals
we
collect
from
the
containers
that
have
been
Auto
discovered
during
this
setup.
The
machine
learning
models
have
already
been
built
and
are
now
being
applied
to
detect.
Various
threats
within
the
environment.
B
Here
we
see
we
have
a
front
end
application
and
a
DNS
server
application.
We
can
also
see
that
we've
that
sack
rock
has
discovered
the
containers
that
are
running
openshift
services
as
well,
which
I'll
come
back
to.
If
we
click
on
the
front-end
application,
we
can
see
the
list
of
specific
alerts
on
the
left
side
that
are
impacting
this
particular
application.
We
can
see.
B
We
can
actually
then
drill
down
into
the
application
and
investigate
at
the
service
level,
and
if
I
zoom
in
at
the
service
level,
you
can
see
that
there
was
a
service
that
was
launched
from
a
my
sequel
image
and
one
that
was
launched
from
a
Python
image,
and
we
can
further
drill
down
into
the
set
of
five
containers
that
make
up
this
particular
service.
And
we
can
see
that
out
of
these
five
containers,
one
is
in
fact
compromised
with
high
severity.
B
This
dashboard
view
allows
us
to
allow
analyst
and
security
operators
to
quickly
understand,
what's
going
on
in
there
open
shipped
environment
and
get
a
visual
overview
of
all
their
containers
and
can
see
it
on
the
left
side.
Now,
looking
at
the
list
of
alerts
that
there
are
a
number
of
different
alerts
ranging
from
policy
violations
to
attacks,
and
if
we
take
a
closer
look
at
this
web
injection
and
exfiltration
alert,
we
can
see
what
application
is
being
impacted
and
when
it
actually
occurred.
B
We
then
go
into
more
detail
and
we
break
down
the
alert
into
different
phases
that
reflect
the
attacks
progression
based
on
different
indicators.
In
this
case,
in
the
first
phase,
where
there
was
code
injection,
a
URL
encoding,
which
was
subsequently
followed
by
an
outsize
HTTP
payload-
and
we
discovered
these
indicators
through
a
number
of
different
vectors,
including
system
level
data
such
as
system
calls
or
container
level
data,
such
as
docker
events
and
then
correlate
them
using
machine
learning.
B
By
focusing
on
how
these
different
events
relate
to
each
other
were
able
to
surface
the
larger
issue
that
an
analyst
or
security
operator
needs
to
be
focused
on,
and
it's
what
it's?
What
allows
us
to
reduce
the
noise,
the
time
and
effort
that
that
security
teams
need
to
spend
on
responding
to
security
incidents
on
their
containers.
B
These
alerts
also
can
be
integrated
with
your
existing
notification
and
instant
management
systems,
so
these
alerts
can
be
sent
out
to
systems
like
slack
or
pager
duty
service
now
or
whatever
you
might
be
using
in
term
your
security
might
be
using
in
terms
of
instant
management.
If
we
select
this
code
injection
event
and
clicked
event
context,
then
we
can
see
a
number
of
additional
details
that
may
be
useful
from
a
standpoint
of
deeper
use.
Cases
such
as
threat
hunting.
B
Now,
if
we
click
and
move
over
to
the
instance,
view
incident
section,
then
this
provides
a
list
of
all
alerts
that
have
been
generated
within
the
stack
Rock
system
and
if
I
click
on
this
lateral
movement
alert
in
particular,
then
we
can
observe
that
in
this
case,
we
detected
on
indicators
that
include
anomalous.
Network
communication
across
services
that
were
then
followed
by
anomalous
processes
being
launched
in
sack
rocks
also
gives
you
ability
to
create
attack
profiles
for
more
complex
attack
patterns.
B
So
if,
for
example,
we
wanted
to
detect
on
the
threat
I
highlighted
earlier,
that
was
brought
to
our
attention
by
the
large
bank
that
involved
the
code
injection
that
was
followed
by
lateral
movement
prior
to
data
exfiltration.
Then
that's
also
easily
configured
within
our
system
and
then
finally,
we
also
can
easily
search
across
the
cluster
to
find
what
we
are
looking
for.
So
if
I
go
into
this
search,
bar
and
I
search
for
a
front-end.
B
And
I
forgot
to
hit
enter
there,
but
now
we
see
that
these
are
being
surfaced,
so
I
can
see
alerts.
I
can
see.
Services
I
can
see
models
all
associated
with
this
particular
search
term
front-end,
and
the
idea
is
that
at
scale
discovering
information
so
that
you
can
do
proper
analysis
and
investigation
from
the
security
standpoint
is
critical
and
so
sack
Rocksmith
makes
it
easy
to
actually
discover
a
number
of
details
and
lets
you
drill
down
quickly
to
what
you
get.
B
That
concludes
this
morning's
briefing.
I've
enjoyed
the
opportunity
to
share
with
all
of
you
about
how
stacked
rocks
enables
enterprises
using
OpenShift
to
benefit
from
comprehensive
security
with
our
adaptive
threat
protection
platform.
I'd
love
to
hear
from
you,
so
please
feel
free
to
contact
me
by
email
on
either
Twitter
or
LinkedIn
I'll,
be
at
both
dr.
B
A
Thank
ya.
Thank
you
very
much
wailing.
This
is
pretty
pretty
awesome,
it's
sort
of
where
security
meets
McKean
alarming
and
really
takes
it
to
the
next
level
in
terms
of
threat
detection.
It's
really
quite
impressive.
That's
I'd
love
to
learn
more,
maybe
in
a
future
time
about
the
different
machine
learning
algorithms
that
you're
using
to
do
that
and
what
what's
under
the
hood
a
little
bit
in
terms
of
that,
because
I
think
that's
an
interest.
It's
an
interesting
application
of
a
machine
learning
that
you
know
we.
A
We
should
expecting
someone
to
come
up
with
a
great
product
offering
around
it,
and
this
is
really
good
enough
to
see
where
we
go
next
with
this,
because
it's
the
pattern
matching
and
the
stuff
that
we
had
done
in
in
the
past
is
pretty
static,
and
this
really
gives
it
an
another
opportunity
to
learn
from
the
universes
and
the
changes
that
are
happening
under
the
hood
and
finding
new
new
patterns
and
applying
them.
So
this
is
really
interesting
stuff.
Yes,.
A
What
we
discipline
we
actually
dip
today,
our
briefing
earlier
this
week
on
tensorflow
and
using
that
and
creating
workload
appreciate
learning
more
coats
on
open
ships.
So
this
is
interesting
from
the
other
perspective
of
using
that
sort
of
stuff
to
detect
the
security
anomalies
and
alert
people
to
put
that
so
it
it's.
It's
definitely
another
another
wonderful
machine
learning
use
case
and
I
I
think
people
will
be
interested
in
understanding
more
about
the
actual
technology.
A
That's
under
that,
but
without
giving
away
any
trade
secrets
or
whatever
but
I,
think
the
youth
pays
for
machine
learning
in
the
cloud
and
would
the
amount
of
data
that
people
generating
these
days
is
huge.
So
I
look
forward
to
many
more
conversations,
I'm,
not
seeing
any
questions
in
the
chat.
So
now
is
your
chance
if
you
have
one
otherwise
I'm
gonna,
let
you
reach
out
and
talk
to
weigh
that
and
meet
everybody
face
to
face
in
December
at
the
open
ship,
Commons
gathering.