Scaling generative AI with flexible model choices

This
blog
series
demystifies
enterprise
generative
AI
(gen
AI)
for
business
and
technology
leaders.
It
provides
simple
frameworks
and
guiding
principles
for
your
transformative
artificial
intelligence
(AI)
journey.
In
the

previous
blog,
we
discussed
the
differentiated
approach
by
IBM
to
delivering
enterprise-grade
models.
In
this
blog,
we
delve
into
why
foundation
model
choices
matter
and
how
they
empower
businesses
to
scale
gen
AI
with
confidence.

Why
are
model
choices
important?

In
the
dynamic
world
of
gen
AI,
one-size-fits-all
approaches
are
inadequate.
As
businesses
strive
to
harness
the
power
of
AI,
having
a
spectrum
of
model
choices
at
their
disposal
is
necessary
to:

• 
  Spur
  innovation:
  A
  diverse
  palette
  of
  models
  not
  only
  fosters
  innovation
  by
  bringing
  distinct
  strengths
  to
  tackle
  a
  wide
  array
  of
  problems
  but
  also
  enables
  teams
  to
  adapt
  to
  evolving
  business
  needs
  and
  customer
  expectations.
• 
  Customize
  for
  competitive
  advantage:
  A
  range
  of
  models
  allows
  companies
  to
  tailor
  AI
  applications
  for
  niche
  requirements,
  providing
  a
  competitive
  edge.
  Gen
  AI
  can
  be
  fine-tuned
  to
  specific
  tasks,
  whether
  it’s
  question-answering
  chat
  applications
  or
  writing
  code
  to
  generate
  quick
  summaries.
• 
  Accelerate
  time
  to
  market:
  In
  today’s
  fast-paced
  business
  environment,
  time
  is
  of
  the
  essence.
  A
  diverse
  portfolio
  of
  models
  can
  expedite
  the
  development
  process,
  allowing
  companies
  to
  introduce
  AI-powered
  offerings
  rapidly.
  This
  is
  especially
  crucial
  in
  gen
  AI,
  where
  access
  to
  the
  latest
  innovations
  provides
  a
  pivotal
  competitive
  advantage.
• 
  Stay
  flexible
  in
  the
  face
  of
  change:
  Market
  conditions
  and
  business
  strategies
  constantly
  evolve.
  Various
  model
  choices
  allow
  businesses
  to
  pivot
  quickly
  and
  effectively.
  Access
  to
  multiple
  options
  enables
  rapid
  adaptation
  when
  new
  trends
  or
  strategic
  shifts
  occur,
  maintaining
  agility
  and
  resilience.
• 
  Optimize
  costs
  across
  use
  cases:
  Different
  models
  have
  varying
  cost
  implications.
  By
  accessing
  a
  range
  of
  models,
  businesses
  can
  select
  the
  most
  cost-effective
  option
  for
  each
  application.
  While
  some
  tasks
  might
  require
  the
  precision
  of
  high-cost
  models,
  others
  can
  be
  addressed
  with
  more
  affordable
  alternatives
  without
  sacrificing
  quality.
  For
  instance,
  in
  customer
  care,
  throughput
  and
  latency
  might
  be
  more
  critical
  than
  accuracy,
  whereas
  in
  resource
  and
  development,
  accuracy
  matters
  more.
• 
  Mitigate
  risks:
  Relying
  on
  a
  single
  model
  or
  a
  limited
  selection
  can
  be
  risky.
  A
  diverse
  portfolio
  of
  models
  helps
  mitigate
  concentration
  risks,
  helping
  to
  ensure
  that
  businesses
  remain
  resilient
  to
  the
  shortcomings
  or
  failure
  of
  one
  specific
  approach.
  This
  strategy
  allows
  for
  risk
  distribution
  and
  provides
  alternative
  solutions
  if
  challenges
  arise.
• 
  Comply
  with
  regulations:The
  regulatory
  landscape
  for
  AI
  is
  still
  evolving,
  with
  ethical
  considerations
  at
  the
  forefront.
  Different
  models
  can
  have
  varied
  implications
  for
  fairness,
  privacy
  and
  compliance.
  A
  broad
  selection
  allows
  businesses
  to
  navigate
  this
  complex
  terrain
  and
  choose
  models
  that
  meet
  legal
  and
  ethical
  standards.

Selecting
the
right
AI
models

Now
that
we
understand
the
importance
of
model
selection,
how
do
we
address
the
choice
overload
problem
when
selecting
the
right
model
for
a
specific
use
case?
We
can
break
down
this
complex
problem
into
a
set
of
simple
steps
that
you
can
apply
today:

1. 
   Identify
   a
   clear
   use
   case:
   Determine
   the
   specific
   needs
   and
   requirements
   of
   your
   business
   application.
   This
   involves
   crafting
   detailed
   prompts
   that
   consider
   subtleties
   within
   your
   industry
   and
   business
   to
   help
   ensure
   that
   the
   model
   aligns
   closely
   with
   your
   objectives.
2. 
   List
   all
   model
   options:
   Evaluate
   various
   models
   based
   on
   size,
   accuracy,
   latency
   and
   associated
   risks.
   This
   includes
   understanding
   each
   model’s
   strengths
   and
   weaknesses,
   such
   as
   the
   tradeoffs
   between
   accuracy,
   latency
   and
   throughput.
3. 
   Evaluate
   model
   attributes:
   Assess
   the
   appropriateness
   of
   the
   model’s
   size
   relative
   to
   your
   needs,
   considering
   how
   the
   model’s
   scale
   might
   affect
   its
   performance
   and
   the
   risks
   involved.
   This
   step
   focuses
   on
   right-sizing
   the
   model
   to
   fit
   the
   use
   case
   optimally
   as
   bigger
   is
   not
   necessarily
   better.
   Smaller
   models
   can
   outperform
   larger
   ones
   in
   targeted
   domains
   and
   use
   cases.
4. 
   Test
   model
   options:
   Conduct
   tests
   to
   see
   if
   the
   model
   performs
   as
   expected
   under
   conditions
   that
   mimic
   real-world
   scenarios.
   This
   involves
   using
   academic
   benchmarks
   and
   domain-specific
   data
   sets
   to
   evaluate
   output
   quality
   and
   tweaking
   the
   model,
   for
   example,
   through
   prompt
   engineering
   or
   model
   tuning
   to
   optimize
   its
   performance.
5. 
   Refine
   your
   selection
   based
   on
   cost
   and
   deployment
   needs:
   After
   testing,
   refine
   your
   choice
   by
   considering
   factors
   such
   as
   return
   on
   investment,
   cost-effectiveness
   and
   the
   practicalities
   of
   deploying
   the
   model
   within
   your
   existing
   systems
   and
   infrastructure.
   Adjust
   the
   choice
   based
   on
   other
   benefits
   such
   as
   lower
   latency
   or
   higher
   transparency.
6. 
   Choose
   the
   model
   that
   provides
   the
   most
   value:
   Make
   the
   final
   selection
   of
   an
   AI
   model
   that
   offers
   the
   best
   balance
   between
   performance,
   cost
   and
   associated
   risks,
   tailored
   to
   the
   specific
   demands
   of
   your
   use
   case.

Download
our
model
evaluation
guide

IBM
watsonx™
model
library

By
pursuing
a
multimodel
strategy,
the
IBM
watsonx
library
offers
proprietary,
open
source
and
third-party
models,
as
shown
in
the
image:

This
provides
clients
with
a
range
of
choices,
allowing
them
to
select
the
model
that
best
fits
their
unique
business,
regional
and
risk
preferences.

Also,
watsonx
enables
clients
to
deploy
models
on
the
infrastructure
of
their
choice,
with
hybrid,
multicloud
and
on-premises
options,
to
avoid
vendor
lock-in
and
reduce
the
total
cost
of
ownership.

IBM®
Granite™:
Enterprise-grade
foundation
models
from
IBM

The
characteristics
of
foundation
models
can
be
grouped
into
3
main
attributes.
Organizations
must
understand
that
overly
emphasizing
one
attribute
might
compromise
the
others.
Balancing
these
attributes
is
key
to
customize
the
model
for
an
organization’s
specific
needs:

1. Trusted:
   Models
   that
   are
   clear,
   explainable
   and
   harmless.
2. Performant:
   The
   right
   level
   of
   performance
   for
   targeted
   business
   domains
   and
   use
   cases.
3. Cost-effective:
   Models
   that
   offer
   gen
   AI
   at
   a
   lower
   total
   cost
   of
   ownership
   and
   reduced
   risk.

IBM
Granite
is
a
flagship
series
of
enterprise-grade
models
developed
by
IBM
Research®.
These
models
feature
an
optimal
mix
of
these
attributes,
with
a
focus
on
trust
and
reliability,
enabling
businesses
to
succeed
in
their
gen
AI
initiatives.
Remember,
businesses
cannot
scale
gen
AI
with
foundation
models
they
cannot
trust.

View
performance
benchmarks
from
our
research
paper
on
Granite

IBM
watsonx
offers
enterprise-grade
AI
models
resulting
from
a
rigorous
refinement
process.
This
process
begins
with
model
innovation
led
by
IBM
Research,
involving
open
collaborations
and
training
on
enterprise-relevant
content
under
the
IBM
AI
Ethics
Code
to
promote
data
transparency.

IBM
Research
has
developed
an
instruction-tuning
technique
that
enhances
both
IBM-developed
and
select
open-source
models
with
capabilities
essential
for
enterprise
use.
Beyond
academic
benchmarks,
our
‘FM_EVAL’
data
set
simulates
real-world
enterprise
AI
applications.
The
most
robust
models
from
this
pipeline
are
made
available
on
IBM®
watsonx.ai™,
providing
clients
with
reliable,
enterprise-grade
gen
AI
foundation
models,
as
shown
in
the
image:

Latest
model
announcements:

• 
  Granite
  code
  models:
  a
  family
  of
  models
  trained
  in
  116
  programming
  languages
  and
  ranging
  in
  size
  from
  3
  to
  34
  billion
  parameters,
  in
  both
  a
  base
  model
  and
  instruction-following
  model
  variants.
• Granite-7b-lab:
  Supports
  general-purpose
  tasks
  and
  is
  tuned
  using
  the
  IBM’s
  large-scale
  alignment
  of
  chatbots
  (LAB)
  methodology
  to
  incorporate
  new
  skills
  and
  knowledge.

Try
our
enterprise-grade
foundation
models
on
watsonx
with
our
new

watsonx.ai
chat
demo.
Discover
their
capabilities
in
summarization,
content
generation
and
document
processing
through
a
simple
and
intuitive
chat
interface.

Learn
more
about
IBM
watsonx
foundation
models