►
Description
In this 41-minute all audiences-focused demo, Microsoft engineers Sean Squires, James Eccles and Bert Jansen, deliver a use-it-now update on SharePoint Syntex – the add-on that builds on the content services capabilities already provided in SharePoint with an infusion of AI to automate and augment the classification of content – understanding, processing, compliance. Demos delivered on building and publishing a document understanding model using UI and on downloading a sample model, publishing and processing content using PowerShell Commandlets or APIs. This PnP Community demo is taken from the SharePoint monthly community call recorded on May 11, 2021.
Demo Presenters:
• Sean Squires (Microsoft) | @iamseansquires
• James Eccles (Microsoft) | @jimdeccles
• Bert Jansen (Microsoft) | @o365bert
Supporting documents:
• Web - SharePoint Syntex | aka.ms/SharePointSyntex
• Blog – Syntex blog | aka.ms/SharePointSyntex/blog
• (Soon) Discussion – Yammer Syntex Community | aka.ms/SharePointSyntex/community
• Roadmap – Syntex Roadmap | aka.ms/SharePointSyntex/roadmap
• Documentation - Introduction to Microsoft SharePoint Syntex | aka.ms/SharePointSyntex/documentation
• Documentation – Scenario: Manage contracts using a Microsoft 365 solution | https://docs.microsoft.com/en-us/microsoft-365/contentunderstanding/solution-manage-contracts-in-microsoft-365
• SDK - Working with SharePoint Syntex | https://pnp.github.io/pnpcore/using-the-sdk/syntex-intro.html
• (Soon) Samples - GitHub: https://github.com/pnp/syntex-samples
• Learning – Virtual Hub | https://adoption.microsoft.com/virtual-hub/search?_sft_vh_product=sharepoint-syntex
Learn more:
• Learn more about the Microsoft 365 Patterns and Practices community at: http://aka.ms/m365pnp
• Visit the Microsoft 365 Patterns and Practices community YouTube channel: https://aka.ms/m365pnp-videos
A
Thank
you
for
your
time
and
the
opportunity
to
come
chat
with
you
about
one
of
my
favorite
products
right
now,
because
I
I've
been
working
on
sharepoint
syntax
for
the
last
couple
years
and
I'm
very
excited
to
share
with
you
just
for
those
of
you
who
are
new
to
it.
What
it
is
very
briefly
and
then
we'll
get
to
some
live
demos,
so
you
can
actually
see
what
it
is,
but
also
give
you
some
updates,
because
ever
since
we
released
it,
we
have
continued
to
develop
and
provide
updates.
A
And
in
fact
we
have
some
more
coming
later
this
month
that
I'll
touch
on
in
just
a
few
minutes.
So
sharepoint
syntax
is
a
new
add-on
for
those
who
aren't
familiar
with
it.
That
we
announced
at
last
falls
ignite
and
by
add-on,
meaning
you
have
to
have
sharepoint
and
then
this
is
something
that
you
can
add
to
your
tenant
to
add
additional
content
service
functionality
and
the
way
we
think
about
sharepoint.
You
know,
sharepoint
is
already
a
content.
A
You
know
a
market
leader
in
content
services,
but
what
syntax
does
is
it
really
builds
on
a
lot
of
the
powerful
content
management
capabilities
we
provide
in
the
product
and
really
infuses
it
with
ai.
In
many
ways
we
talk
about
how
syntax
can
be
perceived,
at
least
with
where
we're
at
right
now
and
where
we're
making
investments.
A
You
know
is
when
you
have
these
models
that
can
automate
that
classification
extraction
of
information.
It
can
really
augment
the
search,
because
now
you've
got
metadata
and
it's
consistent
and
it's
something
that
can
augment
your
search.
It
can
enhance
business
process
automation
by
facilitating.
You
know
when
you're
providing
power,
automate
flows.
A
Having
consistent
reliable
metadata
to
trigger
those
flows,
and
then,
of
course,
we
work
very
closely
with
the
information
governance
group,
and
so
we've
worked
very
hard
to
provide
integration
with
the
compliance
and
microsoft
information
protection
labels,
so
that
you
can
again
use
this
metadata
and
the
content
classification
to
drive
consistency
in
how
your
content
is
being
labeled
for
retention
and
sensitivity
purposes
and
again,
as
we
think
about
what
we
were
looking
to
do
with
syntax.
You
know
again
building
on
the
content
services
capabilities
that
we
provide
in
sharepoint.
A
A
lot
of
factors
went
into
where
we
wanted
to
orient
our
efforts
first
and
a
lot
of
it
was
really
just
looking
at
the
landscape
of
content
services
and
seeing
opportunities
where,
frankly,
sharepoint,
as
this
really
robust
collaborative
platform,
could
also
be
a
place
for
transactional
content
management
and
to
really
start
to
consolidate
other
types
of
business
content.
That
organizations
are
producing
and
needing
to
store,
manage
and
retrieve
content
is
being
created
at
such
an
incredible
pace
that
it's
you
know
daunting
and
difficult
frankly,
to
have
to.
A
You
know,
rely
on
your
users
to
constantly
when
they
put
stuff
into
sharepoint.
To
make
sure
it's
in
the
right
location
that
it's
properly
classified,
that
it's
got
all
the
right
labels
and
metadata
that
the
right
processes
are
implemented
on
those
libraries
to
ensure
that
the
business
runs
smoothly,
and
so
the
more
that
we
can
provide
mechanisms
to
really
facilitate
that
and
ai
models
to
automate
that,
then
we
believe
that
we
can
just
improve
the
overall
content
services
and
capabilities
overall
and
that
data
governance
has
become
really
important.
A
You
know,
in
the
last
couple
of
years
we've
made
great
strides
there
with
the
information
protection
work,
and
so
we
didn't
want
to
undermine
that.
We
want
to
accelerate
and
augment
that,
and
so
it
was
very
important
from
the
beginning
that
the
work
that
we
were
doing
took
that
into
consideration,
and
so
I'm
excited
to
share
an
update
actually
about
the
data
governance,
in
particular.
A
You
can
train
to
understand
the
different
business
file
types,
but
also
expanding
what
those
models
can
extract
and
what
different
file
types
they
understand.
For
example,
one
of
the
two
model
types
james
will
talk
about.
One
of
them
in
a
minute
is
built
natively
in
sharepoint.
That's
document
understanding
models,
those
are
great
for
office
file
types
and
pdf.
We
have
another
model
type,
that's
very
good
for
more
structured
files
like
web
forms
and
invoices,
things
where
you
have
very
clear
key
value
pair
distinctions
and
those
models
are
best
for
scanned
files,
pdf
and
image
files.
A
A
Now
this
eye
chart
is,
you
know
it
is
that
it
is
an
eye
chart,
but
it's
intended
to
sort
of
illustrate
just
that.
There
is
quite
a
bit
too
syntax
in
this
its
current
iteration
and,
as
I
mentioned
a
couple
times,
it
will
continue
to
evolve
both
in
the
depth
of
the
capabilities
that
are
listed
on
this
slide,
as
well
as
in
the
brett
in
er
other
content
services,
areas
that
we're
currently
looking
into
and
or
already
actively
developing,
but
just
to
give
you
a
quick
snap
of
what
you
can
take
away
from
this.
A
In
addition
to
sort
of
that
integration
with
information,
governance,
we've
also
been
doing
a
lot
with
we
own
the
taxonomy
service,
the
manage
metadata
service,
and
so
a
lot
of
the
work
that
we
did
here
takes
advantage
of
those
existing
product
capabilities
and
investments.
As
many
of
you
know,
we've
made
investments
in
modernizing
the
managed
metadata
service,
the
last
18
months,
and
a
lot
of
that
was
in
service
for
folks
who
currently
use
managed
metadata,
but
also
to
enhance
it
for
those
syntax
users,
and
so
we
provide
additional
analytics.
A
We
provide
the
ability
to
enhance
the
content
types
and
how
the
content
types
can
be
distributed
and
even
enforced
in
site
libraries
across
your
tenant
and
there's
some
great
work.
That's
coming
out
later
this
spring,
where
we're
finally
going
to
fix
the
content,
type
syndication
problem
and
also
make
it
much
easier
to
discover
not
only
content
types
in
libraries,
but
also
the
right
types
of
content
types
and
then.
A
Finally,
a
lot
of
this
is
being
done,
as
james
will
show
in
just
a
minute,
with
a
new
site
template
called
the
content
center
and
for
those
old
sharepoint
folks.
You
can
think
of
this
as
sort
of
a
very
intelligent,
modern
document
center.
It
is
a
place
where
we
imagine,
for
some
companies
might
be
a
destination
for
managing
your
content,
but
for
others
it
becomes
a
you
know,
a
watchtower.
A
B
Great
thanks
sean,
so
let's
go
straight
into
demo.
Sean
was
describing.
What
I'm
going
to
show
now
is
how
to
build,
publish
and
publish
a
document
understanding
model
document.
Understanding
models
are
really
suited
towards
unstructured
data.
So
in
the
example
I'm
going
to
share
we'll
look
at
benefit
change
notices.
Here's
an
example
of
the
type
of
file
that
we're
going
to
build
a
model
to
understand.
You'll,
see
it's
it's
unstructured,
I
can't
say
for
the
data.
I
want
to
pull
as
metadata
where
that
might
appear
in
the
document.
B
I'm
going
to
begin
in
the
content
center,
which
is
the
new
site
template
that
sean
was
just
describing
and
use
the
content
center
to
build
my
document
understanding
model
and
then
publish
it
to
one
or
more
sharepoint
libraries,
I'll
I'll
talk
about
the
ui
way
of
publishing
models
in
in
my
section,
the
demo
and
then
we'll
talk
about
some
of
the
options
over
apis
and
through
powershell
with
burp.
In
a
couple
of
minutes.
B
B
We've
also
got
the
option
here
to
associate
a
retention
label
with
my
model,
so
in
this
case
I'll
grab
the
confidential
label.
So
whenever
a
benefit
change
notice
is
processed
by
syntax.
Using
this
model,
it
not
only
will
classify
it
and
extract
the
metadata.
It
will
stamp
the
confidential
label
on
there
as
well
to
preserve
the
life
cycle
of
the
file.
B
When
we're
building
a
document
understanding
model,
there
are
four
steps
that
we
walk
through.
So
first
we
upload
a
training
set
of
files.
Then
we
build
something
called
our
classifier
and
that's
going
to
say
when
a
file
is
executed
against
this
model,
is
it
a
benefit
change?
Yes
or
no,
or
is
it
another
file
type,
such
as
a
contract
or
a
statement
of
work?
How
can
we
make
the
determination
whether
it
is
or
is
not,
that
file
type?
B
So
I've
got
a
small
data
set
here,
what's
interesting
with
sharepoint
syntax,
to
contrast,
it
with
other
similar
technology
that
some
of
you
might
have
worked
with
in
the
machine
learning
space
is
that
we
only
need
a
pretty
small
data
set.
So,
unlike
machine
learning,
where
you
might
be
working
with
hundreds
or
thousands
of.
B
But
again
with
machine
learning,
where
you
might
be
looking
at
hundreds
or
thousands
of
labeled
files
when
we're
working
with,
because
I've
got
a
file
open,
that'll,
be
why
we're
working
with
sharepoint
syntax.
We
only
need
a
small
number
of
files,
a
minimum
of
five
and
what
we're
going
to
do
through
the
process
is
less
around
getting
ai
to
analyze
the
data
set
and
find
patterns.
We're
going
to
use
this
small
data
set
to
walk
through
an
interactive
training
process.
So
the
idea
is
that
I
is
an
expert
in
this
type
of
content.
B
I'm
going
to
use
the
model
to
impart
my
knowledge,
I'm
going
to
teach
the
ai
and
the
ai's
then
going
to
be
able
to
understand
the
content
going
forwards
as
a
bare
minimum
minimum.
We
need
five
files
to
teach
a
document,
understanding
model
as
a
mixture
of
positive
examples
and
negative
examples.
In
my
case,
I've
got
14
files,
so
12
examples
of
a
benefit,
change
and
two
statements
of
work
to
give
the
ai
an
example
of
something
that
isn't
a
benefit
change.
B
Now
I've
got
my
data
set
I'll
move
on
to
the
classifier
and
when
we
work
with
document
understanding
models,
I
really
like
to
use
the
language
of
teaching.
So
imagine,
you're,
teaching
a
human
being
and
the
same
steps
you'll
go
through
what
we
do
when
we're
teaching
the
ai
when
we're
teaching
syntax
how
to
build
a
model.
B
So
yeah
I've
got
pretty
small
children.
So
when
I'm
teaching
them
how
to
do
things
like
recognize
shapes
or
animals,
first
I'll
show
them
examples
and
then
you'll
explain
the
difference
between
them.
So
maybe,
if
I'm
teaching
the
difference
between
a
dog
and
a
cat,
my
children
are
very
small.
So
that's
the
level
we're
at
I'll
show
them
some
pictures
of
dogs,
some
pictures
of
cats,
so
they
can
identify
the
difference
and
then
maybe
I'll
I'll
start
to
explain
the
difference.
So
dogs
might
tend
to
be
bigger
or
cats
can
climb.
B
Trees
and
dogs
can't
I'll
I'll
point
out
the
attributes
of
both
to
help
them
work
out
for
themselves
in
the
future.
We're
going
to
do
exactly
the
same
with
syntax.
First,
we're
going
to
give
examples
in
this
case
we're
going
to
label
some
of
the
documents
and
then
we're
going
to
explain
how
we
came
to
that
conclusion.
We'll
use
what
we
call
explanations
to
provide
additional
context
and
syntax
will
use
a
combination
of
those
explanations
and
the
labeling
that
I
do
to
build
the
model.
B
I'm
looking
at
the
labeling
interface
at
the
moment.
On
the
left
hand,
side
you'll
see
the
files
that
I
have
in
my
training
set
and,
on
the
right
hand,
side
a
preview
pane
of
the
file
now
for
the
preview
pane,
we've
hidden
all
of
the
rich
text,
all
the
rich
formatting
from
the
document
we're
only
interested
in
the
text
when
we're
training,
syntax
so
to
make
the
training
process
easier.
We've
we've
hidden
out
all
that
rich
detail.
B
I
could
hit
the
view
original
button
to
get
into
that
original
version
of
the
file
at
any
time,
but
for
now
we're
only
interested
in
the
text
and
it's
a
pretty
simple
step
for
labeling.
I
need
to
go
through
at
least
five
of
these
files
and
say
yes
or
no,
whether
or
not
it
is
an
example
of
a
benefit
change.
B
Next
is
training
so
again
I've.
Given
my
ex
my
examples
now,
I
need
to
show
my
thinking
show
my
working
out
and
the
explanations
are
interesting
when
I
explain
this
to
customers
oftentimes,
they
get
bogged
down
with
other
technologies,
they're
used
to
and
trying
to
apply
some
of
those
thinking
patterns
to
syntax,
so
they'll,
think
of
explanations
as
being
regular
expressions
or
different
types
of
query
statements
you're
having
to
build
rules
that
will
say
exactly
where
text
appears
within
a
document
and
the
reality
of
syntax
is
that's.
That's
not
the
case.
B
What
explanations
are
are
hints
they're
suggestions
of
how
the
ai
might
identify
documents
or
the
attributes
in
them
again
think
about
how
you're
teaching
a
person
you
won't
necessarily
explain
to
a
person
how
many
characters
into
a
document.
A
particular
phrase
is
you'll
tell
them
where
you
might
identify
a
phrase
or
the
type
of
language
you'll
see
in
a
document
that
tells
you
that
it's
a
benefit,
change
or
a
contract,
and
so
on.
B
B
This
isn't
to
say
that
that
phrase
has
to
appear
in
every
document,
but
this
is
an
example
of
the
type
of
language
that
will
indicate
that
it's
benefit,
change
and
syntax
will
use
this
explanation,
plus
the
labeling.
I
did
in
the
first
step
to
help
build
the
model
on
the
bottom.
You
can
see
for
each
of
the
files,
I've
labeled
we've
got
the
predicted
label
and
an
evaluation.
So
the
evaluation
is
what
the
ai
would
do
based
on
the
instructions
it's
been
given.
So
does
it
agree
with
me
yes
or
no?
B
B
I
can
move
to
the
testing
stage
and
use
the
rest
of
my
data
set
to
do
some
more
testing.
Now.
That
was
a
pretty
simple
example:
let's
move
to
an
extractor
to
show
you
something
a
little
bit
more
complex
like
ramp
up
the
complexity
just
a
little
bit
when
I
create
extractors
they
map
to
the
columns
which
will
exist
on
the
sharepoint
library
that
I
publish
to
so
I
can
create
many
extractors.
Each
one
will
be
a
separate
column
in
this
demo,
I'll
just
create
one
for
time,
but
you'll
see
some
of
the
ideas
here.
B
So
again,
I
can
map
this
to
either
a
new
or
an
existing
column.
I
can
set
what
data
type
I'm
going
to
be
extracting
from
the
particular
file
in
this
case,
let's
grab
the
effective
date
of
the
contract
change,
and
this
might
be
useful
once
we
extract
the
metadata
for
doing
things
like
triggering
some
automation,
so
we
might
need
to
do
notifications
based
on
that
date,
we
might
want
to
sort
or
filter
or
build
a
view
formatter
on
the
library
which
is
using
this
date
column
to
provide
some
improved
navigation
and
readability.
B
And
move
on
to
my
training
stage
before
I
start
to
build
some
explanations:
just
notice.
On
the
right
hand,
side
we've
still
got
a
blue
highlight
on
the
on
the
preview,
and
that
shows
me
what
I
labeled
in
the
first
step.
As
I
start
to
layer
on
explanations,
we'll
also
see
a
green
highlight
on
the
on
the
preview
and
the
green
highlight
shows
what
the
ai
would
extract
from
this
document,
and
we
want
to
see
the
two
things
match
up.
B
B
You'll
see
that's
evaluating
based
on
the
instructions
I've
given
in
the
bottom
left,
and
what
you
might
be
surprised
to
see
now
is
that
we
have
four
out
of
five
mismatches.
So,
given
that
I've
added
a
date,
you
might
be
pretty
surprised
by
that,
because
there
is
a
date
in
this
file.
I've
told
syntax
that
it's
looking
for
a
date,
so
why
hasn't?
It
picked
out
this
particular
date,
and
this
reinforces,
hopefully
the
the
idea
that
we're
not
building
regular
expressions
or
query
statements.
B
These
are
in
the
same
way
that
you
teach
a
person
hints
at
how
to
find
the
right
data.
In
this
case,
I've
told
syntax
that
it's
a
date
it's
looking
for,
but
there's
no
context
here
to
say
which
date
in
this
file
am
I
looking
for.
How
do
I
know
this
is
the
effective
date
rather
than
the
date.
The
document
was
written.
B
B
We've
got
the
green
and
the
blue
highlights
matching.
If
I
look
at
my
mismatch,
though,
what
we
can
see
is
that
it
thinks
there's
an
effective
date
in
the
statement
of
work,
so
we
need
to
give
additional
cues
additional
linguistic
clues
and
hints
to
the
ai
in
order
for
it
to
disambiguate.
So
again
we
can
add
more
explanations,
either
using
the
automatic
phrase,
lists
or
manual
phrase
lists.
B
So
again,
let
me
grab
the
automatic
phrase
list
for
the
after
label
now
have
accuracy
across
all
of
my
five
test
files.
We
expand
out,
look
at
the
rest
of
the
training
set
and
then
finally,
we
can
publish
this
to
a
sharepoint
library.
Now
I'm
going
to
cheat
for
time
and
go
to
an
example
that
I
created
earlier
on.
So
this
is
using
the
same
test
set,
but
I've
created
a
number
of
more
a
number
of
more
extractors
here
as
well.
C
B
We've
also
created
a
new
view
for
this
document
library,
so
we've
changed
the
default
view
to
be
one
based
on
benefit
changes.
The
model
I've
created,
which
shows
all
those
columns,
as
well
as
the
content
type
and
the
confidence
score
and
classification
date
time,
which
are
additional
system
columns.
We
generate
within
sharepoint
syntax
now
over
the
next
couple
of
minutes.
D
Thanks
james
great
demo,
really
shout
how
you
can
build
the
model
and
configure
how
to
tweak
it,
how
to
kind
of
start
from
scratch.
Now,
in
my
demo,
I'm
going
to
focus
more
on
the
automation
paths.
Imagine
I
have
a
model
already
and
I
want
to
automate
all
right.
Good
automation,
automation
requires
apis,
so
this
all
starts
with
apis
and
the
current
apis
are
sharepoint
rest
apis.
Here
you
see
some
examples
that
you
can
use
to
publish
a
model
to
a
library.
D
What
we
then
did
is
we
extended
the
bmp
core,
sdk
our.net,
open
source
sdk
to
make
it
easier
to
use
those
apis.
So
we
have
a
typed
model
which
allows
you
to
just
work
with
syntax,
get
a
model,
apply
a
model
to
a
library
process,
the
content
in
the
library
and
so
on.
All
these
things
can
be
done
using
ant.net
code,
but
if
you
prefer
the
apis
once
the
computation
there,
you
can
just
do
this
in
any
language
that
you
want.
D
D
D
So,
since
the
the
the
model,
the
content
understanding
model
is
just
a
pinp
template,
you
can
just
call
them.
Workbench
beside
template
and
boom
is
being
added
to
your
content
center.
It
has
to
be
on
the
center
side.
Obviously
you
can't
add
this
to
some
somewhere
else,
but
once
you
do
that
you
have
it
in
the
content
center
and
since
we
bring
not
just
the
model
but
also
the
training
data,
everything
is
there.
D
So
you
already
see
how
the
model
is
trained
and
then
you
can
play
around
with
it
and
learn
and
refine
and
make
it
better
add
some
things
you
make
it
your
own
model
once
you're
ready.
You
can
then
deploy
the
model.
What
james
did
in
the
demo
was
picking
a
site,
picking
a
library
like
model
applied
to
this
library.
D
D
D
So
first
of
all,
here
is
an
empty
content.
Center
plain
empty,
just
refreshed
nothing
in
there
brand
newly
created
content
center.
So
what
I
first
going
to
do
is
use
one
of
the
samples-
and
this
is
a
preview
of
the
syntax
sample
repo
asset,
we'll
launch
it
probably
this
week,
that's
the
plan
and
it
has
samples,
but,
for
example,
I'm
going
to
use
the
contact
node
to
sample,
which
is
a
pnp
file.
D
So
imagine
this
pnp
file
bloody
being
available
in
your
system.
You
can
then
do
it
like
this.
Let's
go
over
here
so
here
you
see
the
same
pnp
file,
so
I'm
going
to
install
this
model
to
my
content
center
site.
So
first
step
is
connecting
to
my
content
center
site.
If
you
do
like
this,
and
I'm
going
to
authenticate
with
my
demo
account
that
should
go
fairly
quick
and
with
the
next
step
is
applying
the
pnp
package,
the
site
template
to
the
center
side,
using
the
invoke
beam
beside
template
commandlet.
D
This
will
add
the
model
file,
the
classifier
and
also
all
the
training
data.
So
you
get
all
these
documents
as
part
of
this
package
and
both
will
be
added
to
the
right
to
the
correct
location.
So
this
takes
a
couple
of
seconds
to
add
yeah,
let's
go
already
over
here.
Maybe
you
can
already
see
some
content
coming
up
creating
files.
D
D
If
I
open
up
the
model,
I
get
my
first
staff
experience,
I'm
going
to
click
that
away,
but
here
you
see
already
like
a
model
with
trading
data,
fully
trained
as
well.
One
percent
rain.
We
have
the
extractors
like.
If
you
take
an
extractor,
I
can
see
the
highlights
that
happened
and
so
on.
So
everything
is
there,
like.
You
would
have
built
this
up
manually
from
the
get
go,
so
you
have
a
fully
prepped
and
trained
model
that
you
can
then
use
to
further
refine.
D
Now
the
scope
of
this
demo
is
more
around
showing
how
to
automate
things.
So
let
me
go
back
to
powershell
and
let's
just
do
this:
let's
have
a
look
at
our
currently
available
models
in
our
content
center
and
this
one
does
list
our
contract
notice
model
that
we
just
added.
So
that's
great,
that's
correct!
D
Now,
in
the
next
step,
I'm
going
to
deploy
this
model.
So
here
I
have
a
demo
site
with
a
document
library
containing
some
contract
notices.
Some
documents
plain
vanilla,
just
a
standard
document
library
where
I
just
dropped
some
documents
in
okay.
Now
what
happens?
If
I
add
this
model
to
this
library,
I'm
gonna
copy,
write,
syntax
just
did
not
make
any
typos
so
recommended
to
use
this
publish,
dash,
pnp,
syntax
model,
there's
also
an
unpublished
by
the
way,
so
a
publish
will
deploy
the
model
to
a
library.
D
D
This
also
works
in
batch
by
the
way.
So
this
is
like
an
interactive
one,
but
you
can
also
create
a
batch
using
a
new
bnp
badge,
and
then
you
do
publish
this
model
for,
like
one
model
to
10
libraries,
100
live
with
1000
libraries,
they
all
are
badged
together
and
send
us
one
batch
to
the
server
to
process
them
at
once,
which
will
give
you
a
performance
improvement
versus
doing
this
one
by
one
return
code
is
201,
meaning
success.
D
So
going
back
to
our
library,
let's
refresh
and
let's
have
a
look
at
our
view
list-
and
we
see
here
that
there
now
is
like
a
contract
notice
view
this
was
added
by
by
applying
the
model.
I
didn't
choose
to
make
this
a
default
view,
but
you
can
also
use
the
api
say.
This
will
be
the
default
view
and
then
it
will
change
to
this
contract
notice.
By
default.
D
D
Obviously
this
is
an
async
process,
so
it
happens
in
background,
as
james
mentioned
two
to
ten
minutes.
So
it's
not
immediately
popping
up.
Let
us
see
what
happens
during
the
demo,
but
these
get
processed
the
other
ones
which
were
there
are
not
getting
processed
by
default.
So
to
process
those
we
need
to
do
something
more
going
back
to
powershell
before
we
go
to
the
processing
of
the
existing
files.
Let's
just
do
this
get
pdp
syntax
multiplication.
D
Mantlet
as
a
way
for
you
to
see
where
is
this
model
published,
so
this
will
give
you
like
a
list
of
all
the
libraries
all
the
sites
and
libraries
with
this,
but
this
model
is
published
now,
I'm
going
to
switch
to
my
demo
site.
So
today
I
was
as
of
now
I
was
in
the
demo
content
center
side
so
connected
to
the
content
center.
If
you
work
with
models,
you
do
it
from
the
content
center.
D
If
you
work
with
content,
you
do
it
from
the
actual
site.
So
now
I
switch
to
my
syntax
mate,
21
demo
site
and
I'm
going
to
actually
process
all
the
content
in
the
contract
notice
library.
So
this
command,
let
request
dashboard,
classify
and
extract,
will
take
all
the
documents
and
if
they
are
not
yet
processed
by
syntax,
it
will
process
them.
You
can
also
use
minus
force,
or
you
can
say
I
want
to
reprocess
everything
in
the
library,
so
this
is
a
way
for
you
to
backfill
the
columns
for
existing
content.
D
Once
you
apply
a
model
to
an
existing
library
and
again
this
goes
quite
fast.
It
will
just
skew
up
the
request
for
a
server
to
handle.
You
see,
there's
a
ton
of
documents
being
processed
over
here
and
once
this
this
gets
running.
You'll
see
metadata
popping
in
here.
Let
me
just
refresh
this
see
if
there's
already
something-
and
yes
you
see,
things
are
popping
in
so
the
model
is
being
the
model
extraction,
is
working
and
applying
metadata
for
my
document
into
my
columns.
D
C
Before
don't
don't
stop
sharing,
I
will
ask
this
from
sean,
so
sean
just
say
a
positioning
perspective.
So
hopefully
index
is
not
for
a
small
companies,
but
what
is
really
the
sweet
sweet
spot
of
sharepoint
centers?
Can
we
kind
of
go
back
on
basics
and
and
kind
of
talk
about
that?
One
slightly
few
minutes
sure.
A
I
don't
know
if
it's
so
much
that
it's
not
for
small
businesses.
I
think
it's
more
going
to
be
dictated
by
the
use
case
and
the
content.
You
know
I
mean,
if
you're
a
small
five-person
accounting
firm
but
you're,
processing
lots
of
documents,
and
you
don't
want
to
do
that
work
manually.
There's
there's
a
need
for
syntax
that
I
could
see
being
fulfilled
there.
So
I
think
it's
going
to
be
more
around
the
content
in
the
use
case.
A
We
see
it
as
you
know,
something
where
we
also
know
that
certainly
customers,
we've
talked
to
and
folks
in
our
preview
program,
have
also
shared
that
they
often
have
to
rely
on
different
products
to
you
know
perform
some
of
these
same
functions,
and
so
I
won't
you
know
shy
away
from
the
fact
that
part
of
the
reason
why
we're
doing
this
is
because
we
see
an
opportunity
to
you
know,
make
it
easier
for
folks.
You
know
to
you're
already
storing
a
lot
of
your
content
to
them
365
and
on
the
sharepoint
stack.
A
Let's
make
it
easier
for
you
to
do
those
things
without
having
to
rely
on
a
bunch
of
other
tools
to
do
pre
or
post
content
processing.
So
that's
a
driver
there
as
well,
and
certainly
an
aspect
of
the
product
development
that
dictates
what
we're
looking
at
and
what
we're
you
know
investing
in
in
terms
of
building
out
the
product.
C
Yeah,
absolutely,
and-
and
is
it
further
than
say
that
what
you
were
saying
actually
that's
a
good
point
that
it's
really
about
the
volume
of
the
documents
and
forms
and
what
is
actually
being
processed,
and
the
whole
point
of
here
is
that
well,
somebody
might
be
saying
again,
another
licensed
product
and
all
of
that.
But
the
whole
point
of
this
licensing
is
that
it
it's
going
to
be
cheaper
for
you
to
use
the
machine
learning
rather
than
somebody
manually
setting
up
the
metadata
and
if
that's
not
the
case,
don't
buy
the
license,
of
course.
A
Yeah,
that's
that's
a
great
way
of
summarizing
yeah
for
sure
and
and
in
fact
I
would
also
add
that
that
notion
of
content
volume
also
highlights
where
you
know.
I've
answered
a
few
questions
that
yes,
the
licensing
currently
is
five
dollars
per
person
per
month.
Most
of
sharepoint
is
per
person
licensing,
but
we
recognize,
as
I'm
sure
some
of
you
do
as
well
as
you've
got.
You
know
as
we've
gone
through
this
you're
like
wait
a
minute.
How
does
that
match
with
a
small
company?
A
That's
processing
a
hundred
thousand
documents
a
month
versus
maybe
a
large
company,
that's
processing
a
hundred
documents
a
month.
You
know
the
user
equation,
doesn't
quite
the
math
doesn't
work
out,
and
so
one
of
the
things
that
we
are
looking
at
is
you
know
how
to
accommodate
based
on
consumption
and
volume,
because
we
think
that
that's
going
to
be
a
more
important
factor
than
you
know,
the
one
or
a
hundred
people
that
are
using
the
product
and
so
we're
getting
that
feedback
from
folks
like
you.
A
C
Absolutely
that's
actually
a
good
way
of
actually
saying
that
as
well.
It's
just
a
recap
for
everybody:
it's
not
that
the
licensing
is
there,
but
we
want
that
license
to
be
a
fair,
that
it
makes
sense
for
our
customers
as
well,
and
that's
the
most
important
thing
is
to
be
able
to
then
give
us
feedback
related
on
all
of
the
costs
and
structures
and
models.
What
we
have
and
then
functionality
is
what
we
have
so
that
we
can
provide
additional
value
for
our
customers,
because
everything
what
we
do
is
for
our
customers.
So,
yes,.
A
Those
are
a
part
of
that
included,
license
cost
with
form
processing
we
integrate
with
ai
builder,
which
is
a
new
service
on
the
power
platform
and
that
uses
power
platform
credits.
So
if
you
don't
already
have
power
platform
credits,
you
will
either
need
to
purchase
those
or,
if
you
have
at
least
300
syntax
licenses,
you
automatically
get
a
million
service
credits
renewable
each
month.
So
there's
more
of
that
on
the
licensing
detail
page,
but
that
question
comes
up
just
wanted
to
call
attention
to
that.