A.I PROJECT CYCLE

this is a slide on the conceptual study of A. I project cycle which includes the deeper definition of ai project cycle and its substages with real-life examples, So if you like my slides then don't forget to like and share the slide

1. <
Pig
o)
5
=)
5
=)
z
5
E
&
Ú
zZ
le]
[3]
m
a
E

wy
=]
o
>
©
E
9
u
2
=

| a"

7 AI PROJECT
> CYCLE

|
y El
-—

2. Problem Scopi

Data acquisition

Data exploration

Modelling

Evaluation and Deployment

3. THE 5 CHARACTERSTICS FOR THE PROJECT CYCLE

Problem

Scoping

4. plex FE is decomposed into
etting a deep dive into the project

5. DATA (STAGE-2)

= Data
= Data stage can be divided into 2 sub-stages:

» Data Acquisition

e Data Exploration

+ Data acquisition can be divided to 2 sub-stages
= Data Requirement

= Data Correction

6. DATA ACQUISITION (STAGE — 2.1)

As the above term clearly mentions, this stage is about acquiring data
for the project. Let's first understand what is the meaning of data. The
Data is a piece of information , facts and statistics collected together
for reference or analysis

Data Requirement

Every Al project will have different data requirements based on the
specifics of the project

Here, we analyze format in which the data is required
Identifying the sources of data

Finding out the frequency of the data inputs (i.e.) one time data or we
need regular data.

7. DATA ACQUISITION AND DATA
EXPLORATION (STAGE — 2.1 & 2.2)

= Data Collection:

= The collection of data will involve a number of people like
developers, data architects, database administrator etc. In
this stage, has links to be established with people,sources
and methods found for extracting the required data from
these sources
= DATA EXPLORATION :
= The collected data has to be explored before it is useful.
This stage deals with verification of the collected data to
see if it meets our requirement or not. This involves finding
out if the collected data is according to the specifications
decided by us and is free from errors

ATA VA

:

8. MODELING (STAGE - 3)

Data modeling is the third stage of the Al project cycle. It
refers to selecting the specific algorithms and building Al
models for working with the data.

= The selection model basically depends on the problem of
data being used.

= There are different models like neural network and
decision.

= Itis of 2 types:
= Rule based approach
= Machine learning approach

9. EVALUATION AND DEPLOYMENT (STAGE-4)

= The final L
system. This step inv« >
there are any errors, see if the
deploying them in AWS Cloud.

cle involves evaluation i.e. testing of the system, to deploy the
tem to see now the system responds to the data, check if
established goals l.e: devloping apps and

Choose ML Model

10. °
=
2
z
<
ac
ke