The phases of an AI project cycle

The phases of an AI project cycle include :-

1.problem identification,

2. problem scoping,

3. data gathering,

4. data exploration,

5. data modelling,

6. evaluation and deployment.

Problem identification:

Finding the actual, root cause of the issue entails.
Clearly characterising the problem and identifying its root cause.
Correctly framing the issue (framing is a structural representation of a problem or an issue; it involves talking about and elaborating on the issue's context to foster better understanding.)
It's not always easy to see the problem. Although you might think the issue is merely at the surface, this isn't always the case.
The problems are frequently not immediately obvious; they may seem little, but upon further examination, we discover that the problems are complicated and that the initial problems were insignificant.

Problem scoping:

While we are just starting off, there are always a few problems with the work or technique.
These problems could be little or significant; sometimes we decide to ignore them, other times we need urgent fixes.
Problem scoping is the process through which we determine the problem that has to be solved.

The 4Ws Canvas:

The 4Ws Canvas is an effective tool for problem scoping.
Basically, inquiries that aid in a better, more organised understanding of the issues.

Who?: refers to the individuals who are involved in, impacted by, and attempting to remedy the situation.

What?: refers to the specifics of the problem and your understanding of it. What sort of problem is it specifically? Is it easy to understand? How can you tell if there is a problem? What evidence is there that the problem exists? etc. You need to figure out what the problem is exactly right away.

Where?: As these elements are related to the problem, pay attention to the problem's context, situation, or location.

Why?: refers to the need for a solution, the benefits that stakeholders will get from the solution and how those benefits will help stakeholders and society as a whole, as well as what will happen to stakeholders after the problem has been solved.

Data acquisition:

Raw facts, figures, information, or statistics are referred to as data.
Acquisition: Therefore, data acquisition refers to gathering the information needed to solve an issue.
Acquiring data for the project is referred to as acquisition.

In this example, there are two types of data: primary and secondary

Primary data is information that has been collected directly from the source of the information.
The majority of the time, information is gathered especially for a research project, while it may also be made available to the general public for use in other studies.
Primary data is often reliable and accurate.
Primary data is likewise real-time because it is only gathered as needed and not stored.

Information that has already been obtained and made available for use by others is referred to as secondary data.
Secondary data are often easy for researchers and users to get because they are shared publicly.
The information is therefore frequently vague and non-specific, and it may also be out-of-date and inappropriate for the user.

Data exploration:

Data visualisation is used to quickly identify ideas or to highlight areas or patterns that could be looked into further.
The first step in data analysis is data exploration.

Modelling:

Artificial intelligence (AI) models are programmes or algorithms that use a set of data to identify specific patterns.
When presented with sufficient evidence, this enables it to form an opinion or make a prediction.

Evaluation:

The outputs that are created by feeding data into the model and comparing the outcomes with the actual solutions are what are used to assess an API Evaluation's dependability.

Deployment:

Deployment, the action of incorporating a machine learning model into an already-existing production environment, enables you to use data to help you make informed business decisions.