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These questions serve as a structured approach to ensure that all relevant aspects of the problem are considered,
thereby facilitating a more effective and efficient analysis and solution development.
Once you’ve completed the 4W's problem canvas, the next step is to consolidate all the information into a single,
comprehensive template. The problem statement template allows you to compile all the key details into one
format, providing a clear reference point for future use. Problem Statement Template is a structured format to
articulate the problem clearly. It ensures that the problem is well-defined and understood by all stakeholders.
Below is a template with designated spaces to fill in details aligned with your objectives.
4W Problem Statement Template
Our [stakeholders] Who
has a problem that [issue, problem, need] What
when/while [context, situation] Where
An ideal solution would [benefit of solution for them] Why
Stage 2 Data Acquisition
The next stage in the AI project cycle is known as data acquisition. This stage involves gathering raw data, which
is essential for referencing or performing analysis that will guide the project. The process of data acquisition
encompasses the collection of a wide range of data types, including text, numerical values, images, videos, and
audio. These various forms of data can be sourced from multiple places such as the internet, academic journals,
newspapers, and other relevant publications or databases. The goal of data acquisition is to capture accurate
and valuable information that reflects real-world scenarios. This collected data serves as a foundation of the
project that provides valuable insights and enabling the improvement of the project’s performance and the
development of more precise AI solutions.
Stage 3 Data Exploration
Data exploration is a crucial step that involves analysing large volumes of data to uncover meaningful patterns,
trends, and relationships using various data visualisation and statistical techniques. By transforming raw data
into visual formats such as charts, graphs, and plots, data scientists can more easily interpret and draw insights
from the information. This analytical approach not only helps to reveal underlying structures within the dataset
but also facilitates the identification of anomalies or irregularities, ultimately laying the groundwork for more
informed and effective decision-making in subsequent stages of the AI project.
Stage 4 Modelling
The design phase is an important stage in the AI project cycle, ensuring that AI systems function effectively.
Data modeling is a foundational step within the AI development process, happening before model training. It
involves identifying and defining the relationships between different data entities, ensuring that the AI model
can understand the connections and dependencies within the data. The quality of the data model significantly
impacts the performance of the AI model, as well-structured data enables better learning and more accurate
predictions.
Additionally, comparative analysis is performed to understand the advantages and disadvantages of each
model. This helps in identifying the most effective solution that aligns with the project goals and constraints.
The design phase often involves iterative refinement, where initial models are fine-tuned based on feedback or
preliminary testing.
Revisiting AI Project Cycle & Ethical Frameworks for AI 139

