Page 154 - Ai Book - 10

2 2 AI Project Cycle Project Cycle
AI

u Stages of AI Project Cycle u Data Modelling
u Problem Scoping u Evaluation
u Data Acquisition u Neural Network
u Data Exploration

Artificial intelligence is one of the booming technologies of this digital era. Most of the companies use AI to
accomplish tedious or complex tasks which are difficult for human beings. In general, the term ‘AI’ is a process of
teaching machines to learn, think, decide and act like a human being. The process of developing machines has
different stages that are collectively known as ‘AI Project Cycle.’ Let’s explore the chapter to know more about
different stages of an AI Project.

STAGES OF AI PROJECT CYCLE

In our daily life, we follow step by step procedure to complete a task
from beginning to end. Similarly, we need a project cycle(step by step)
procedure to develop an AI system as it provides us an appropriate Problem Data
framework of planning, organizing, executing and implementing an AI Scoping Acquisition
project.
The steps involved in an AI project life cycle are as follows:

u Problem Scoping: The first step of an AI project life cycle is defining AI Project Data
the scope of a problem. By scoping a problem, we are able to Life Cycle Exploration
develop a working model of how things are. In this step, nature,
complexity level and boundaries of a problem are defined using
4W’s framework— Who, What, Where and Why. Evaluation Modelling

u Data Acquisition: The process of identifying and gathering all the
data requirements for an AI project is called Data Acquisition. Data
Acquisition plays an important role because the whole project is
carried out on the basis of identified requirements.

u Data Exploration: Data Exploration, one of the most important phases, is the process of understanding the
nature of data in terms of quality, characteristics, etc. that you have to work with. Good quality data is a
must for an effective end product.

u Modelling: In the modelling phase, collected data must be analyzed according to the gathered project
requirements. After analysis, we can train the model using appropriate machine-learning algorithms on the
basis of selected datasets.

u Evaluation: The last step of the AI project life cycle is Deployment. Before deployment, the model must be
evaluated because it determines the efficiency of the model.

28
28