What is the Process of Machine Learning (ML) Development?

Machine
learning
has
spurred
a
formidable
revolution
in
technology
by
enabling
interactions
between
humans
and
machines.
The
common
assumption
about
machine
learning
development
process
is
that
it
is
an
extremely
complex
and
technical
process.
For
example,
machine
learning
projects
require
data
processing,
comprehensive
model
training,
deployment,
and
maintenance.

On
the
other
hand,
the
process
of
developing

machine
learning
models
also
requires
attention
to
in-depth
business
acumen,
data
analytics,
model
development,
model
evaluation,
and
skills
in
data
collection
techniques.
Machine
learning
experts
must
know
the
development
lifecycle
and
the
steps
that
offer
structure
to
machine
learning
projects. 

Businesses
can
rely
on
the
commonly
followed
roadmap
for
their
ML
projects
to
come
up
with
cost-effective,
high-quality,
and
sustainable

artificial
intelligence
products.
Let
us
learn
more
about
the
essential
steps
in
a
machine
learning
project
development
lifecycle
and
the
important
factors
for
project
success
in
each
step.

How
Can
You
Start
Building
an
ML
Project?

Development
of
an
AI
model
can
be
complex,
and
people
with
expertise
in
machine
learning
also
struggle
to
achieve
success.
The
ML
development
process
is
complex
and
requires
a
perfect
blend
of
creativity,
diligence,
and
experimentation.
Interestingly,
you
can
simplify
the
complete
process
of
designing,
deploying,
and
managing
ML
models
in
a
general
pattern.
Awareness
of
the
steps
helps
you
understand
the
process
of
ML
model
development
and
improves
your
command
of
the
best
practices
to
guide
your
project
to
success.

Every
step
in
the
machine
learning
development
lifecycle
is
crucial
to
the
success
of
the
project.
Therefore,
it
is
important
to
establish
some
safeguards
in
the
form
of
quality
assurance
frameworks
to
ensure
constant
improvement
and
maintenance
in
adherence
to
constraints
and
specific
requirements.
In
addition,
you
must
have
in-depth
knowledge
of
machine
learning
fundamentals,

deep
learning,
modeling,
and
ML
training
techniques.
Once
you
have
all
the
prerequisites,
you
can
begin
the
development
project
with
the
help
of
the
following
roadmap.

Assessment
of
Goals
and
Project
Planning

The
answers
to
queries
like
“How
is
machine
learning
developed?”
create
curiosity
about
the
underlying
technologies.
On
the
other
hand,
it
is
important
to
ensure
that
you
know
why
you
are
building
a
new
ML
model.
The
first
step
in
any
ML
development
project
begins
with
planning,
as
it
helps
in
assessment
of
scope,
feasibility,
and
success
metrics
of
machine
learning
applications.
It
is
also
important
to
understand
how
the
ML
model
would
address
the
problems
of
a
business.
The
planning
phase
gives
you
time
to
think
about
how
you
can
leverage
machine
learning
to
improve
current
processes.

Another
crucial
aspect
in
the
planning
stage
of
the
ML
development
lifecycle
is
the
cost-benefit
analysis.
You
must
also
establish
a
clear
timeline
for
shipping
the
ML
solution
in
different
phases.
In
addition,
you
must
provide
effective,
measurable
metrics
for
ML
models,
business
indicators,
and
economic
indicators.

On
top
of
it,
the
planning
stage
also
requires
the
creation
of
a
feasibility
report.
The
feasibility
report
must
include
information
about
availability
of
data,
legal
constraints,
explainability,
applicability,
resilience,
and
scalability.
You
must
also
include
information
about
resources
available
for
the
project
in
the
feasibility
report.

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Business Course

Identify
the
Type
of
Data
Required
for
the
Project

The
business
case
for
the
machine
learning
project
helps
build
a
framework
for
designing
the
next
steps
of
the
project.
Data
is
the
lifeblood
of
machine
learning
projects,
and
the
model
development
process
actually
begins
with
identification
of
data
requirements.
ML
models
gain
generalized
insights
from
the
training
data
and
apply
the
knowledge
acquired
from
the
training
process
to
come
up
with
new
predictions.

The
most
important
aspects
for
determining
the
data
needs
include
the
type
and
quantity
of
data,
quality
of
training
data,
and
the
data
sources.
In
addition,
you
must
also
consider
whether
you
can
utilize
a
pre-trained
machine
learning
model
to
ensure
that
you
make
the
right
choice.

The
process
for
determining
the
data
requirements
of
a
machine
learning
project
also
involves
understanding
the
ways
in
which
the
model
would
work
with
real-world
data.
Would
you
need
data
for
real-time
use
cases
with
higher
performance
requirements?
Such
types
of
questions
can
help
you
determine
data
access
requirements
with
better
accuracy. 

Preparing
the
Data
for
Your
ML
Projects

You
must
pay
attention
to
data
preparation
in
ML
projects
as
it
determines
what
your
machine
learning
model
will
learn.
The
machine
learning
development
process
relies
significantly
on
the
four
pillars
of
data
preparation:
data
collection
and
labeling,
cleaning,
processing,
and
management.
Here
are
some
of
the
important
steps
that
you
must
follow
for
data
preparation
in
ML
development
projects.

• 
  Data
  Collection
  and
  Labelling 

Developers
must
determine
how
they
would
collect
data
by
identifying
the
ideal
data
sources.
Some
of
the
popular
data
sources
include
internal
data,
vendors,
synthetic
data
generation,
and
open-source
platforms.
The
different
data
sources
have
unique
strengths
and
limitations,
and
some
projects
may
require
all
the
data
sources.
After
collecting
the
data,
you
must
label
it
to
make
changes
during
the
development
lifecycle.
Data
collection
and
labeling
is
a
time-intensive
and
intricate
process
that
consumes
majority
of
company
resources. 

• 
  Data
  Cleaning 

Data
cleaning
or
refinement
is
another
prominent
highlight
in
answers
to
“How
is
machine
learning
developed?”
in
the
data
preparation
phase.
It
involves
imputing
missing
values,
reducing
noise,
removing
outliers,
and
analysis
of
data
with
wrong
labels.
You
would
also
develop
data
pipelines
for
automation
of
the
process
and
performing
verification
of
data
quality.

• 
  Data
  Processing 

Data
processing
during
data
preparation
focuses
on
feature
selection
and
management
of
imbalanced
classes.
The
phase
also
involves
data
augmentation,
feature
engineering,
data
scaling,
and
normalization.
Developers
also
ensure
the
storage
and
versioning
of
the
metadata
alongside
data
modeling
during
the
data
processing
stage.
You
will
also
need
feature
stores
and
transformation
pipelines
to
reproduce
the
important
training
data.

The
final
step
in
the
data
preparation
phase
of
the
ML
development
process
emphasizes
data
management. 
It
invites
the
limelight
towards
identification
of
effective
data
storage
solutions.
Developers
can
leverage
solutions
like
ETL
pipelines
to
achieve
a
constant
data
stream
to
support
training
of
models.

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Engineering
the
Model

The
model
engineering
phase
in
the
machine
learning
development
project
involves
the
use
of
information
from
planning
phase
for
developing
and
training
ML
models.
Model
engineering
phase
includes
tracking
model
metrics,
optimization
of
compute
resources
and
storage,
and
achieving
scalability. 

It
is
important
to
know
that
the
ML
development
lifecycle
starts
with
development
of
an
effective
model
architecture
through
extensive
research.
Model
engineering
also
involves
quantification
of
model
metrics
alongside
training
and
validation
of
the
model
on
the
validation
and
training
dataset.
Developers
have
to
focus
on
tracking
experiments,
features,
machine
learning
pipelines,
metadata,
and
modifications
in
the
code.

The
model
engineering
stage
also
involves
model
compression
and
ensembling
alongside
effective
interpretation
of
results
by
leveraging
domain
knowledge
expertise.
Developers
should
also
maintain
clear
documentation
of
features,
ML
experiments,
hyperparameters,
development
environment,
metadata,
and
model
architecture
for
easier
retrieval. 

Assessment
of
the
Model

When
you
have
the
final
version
of
the
model
you
think
would
be
the
best
for
a
public
release,
you
have
to
test
it
on
the
basis
of
different
metrics.
The
model
assessment
stage
helps
in
ensuring
that
the
model
is
ready
to
go
into
production.
Model
assessment
empowers
the
machine
learning
development
process
by
testing
ML
models
on
test
datasets
to
ensure
that
subject
matter
experts
can
find
the
errors
in
responses
by
the
model.
The
model
assessment
step
involves
following
industrial,
legal,
and
ethical
frameworks
for
the
development
of
ML
models.

Model
assessment
also
focuses
on
testing
models
for
resilience
on
real-world
and
random
data.
Therefore,
it
can
help
you
verify
whether
the
model
is
capable
of
generating
faster
responses.
In
addition,
you
should
also
test
the
model
results
against
the
predefined
success
metrics
and
determine
whether
you
can
deploy
the
model.
Proper
documentation
of
all
assessment
processes
helps
strengthen
quality
assurance
and
backup
for
ML
development
projects.

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Deploying
the
ML
Model

The
final
stage
in
the
answers
for
“How
machine
learning
is
developed?”
is
the
deployment
phase.
You
have
to
deploy
the
ML
models
to
the
system
you
have
for
the
ML
project.
Let
us
assume
that
you
have
created
a
model
for
automatic
warehouse
labeling
based
on
product
dimensions.

The
model
would
require
a
computer
vision
model
in
the
existing
warehouse
system
that
would
use
images
for
creating
and
printing
labels.
Developers
can
deploy
ML
models
on
web
browsers,
the
cloud
and
local
servers,
and
edge
devices
or
package
them
as
software
solutions.
The
deployment
process
also
involves
definition
of
the
inference
hardware
and
ensuring
that
you
have
adequate
storage,
computing
power,
and
RAM
for
generating
faster
results.

Final
Words

The
use
of

machine
learning
to
drive
the
AI
revolution
has
invited
curiosity
about
the
working
of
ML
projects.
The
ML
development
process
can
vary
from
one
organization
to
another,
depending
on
the
goals
of
the
project.
However,
the
process
for
developing
ML
models
follows
a
general
pattern
in
which
you
can
find
a
few
distinct
steps.

The
important
steps
in
ML
development
projects
include
planning,
data
requirements
analysis,
data
preparation,
model
engineering,
and
model
assessment.
Enroll
now
in

AI
certification
to
learn
about
the
best
practices
for
updating
and
maintaining
the
ML
models.
Find
more
insights
on
how

machine
learning
models
work
and
build
your
own
project
now.