Kips Class 9 AI Unit 2: AI Project Cycle

Unit 2: AI Project Cycle

Question and Answer
Question 1: Define project.
Answer:
Project is a sequence of tasks that must be completed in order to attain a specific task.

Question 2: Define project cycle.
Answer:
Project cycle is a sequence of tasks that is required to complete in order to attain a specific goal.

Question 3: Differentiate between AI project cycle and IT project cycle.
Answer:

→ AI project cycle
An important distinction in the AI systems is that they learn. This means that the AI project doesn't stop at the deployment stage but rather at this stage the AI project system becomes cyclic.


Artificial Intelligence system can improve themselves over a period of time. This unique functioning means that the AI project cycle planning is different from the start in different areas like budgeting and planning.

→ IT project cycle
Every IT project cycle has four distinct phases i.e.
Phase 1: Designing
Phase 2: Development
Phase 3: Testing
Phase 4: Deployment

Question 4: List the phases of AI project cycle.
Answer:
The AI project cycle doesn't end at deployment stage instead the cycle becomes cyclic.


Question 5: Explain stages of AI project cycle.
Answer:

→ Stage 1: Planning
Planning is indispensable for the success of the projects, and AI projects are no exception.

Problem scoping

1) Business or project requirements: This is the first step in an AI project cycle. The project cycle starts with determining the project requirements which involves asking questions i.e. What objective will the project serve? Who will be benefit from it? Who will be responsible for it? How will it be done? etc.
2) Analytics approach to the project: The second step in the AI project cycle involves finding out how to achieve the project requirements, objectives or goals determined in the first step. This stage involves questions like: Which methodology or strategy will be used? Is our approach to tackling the problems correct? How much data will be needed? etc.

→ Stage 2: Data
Artificial Intelligence systems are dependent on large amount of data. Without this data, we cannot do anything at all. The data stage of the AI project cycle is further divided into:
Data acquisition
The data acquisition is further divided into 2 sub-parts:

3) Data requirement: Every AI project will have different data requirements based on the specifics of the project. The data requirements will be based on different things like:
• The format in which the data is required.
• Identifying individuals who will maintain the data.
• Finding out the security measures related to the data.

4) Data collection:
Based on the data requirements, the data collection will be done. The collection of data will involve a number of people like developers, data architects, database administrator etc. In this stage, links have to be established with people and sources, and methods found for extracting the required data from these sources.

Data exploring

The collected data has to be explored before it is useful. This exploration of the data involves 2 steps:
5) Exploratory data: This stage deals with validating or verification of the collected data to see if it meets our requirements or not. This involves finding out if the collected data:
• Is meeting our needs
• Has some insights that can be used by us
• Is free from errors or emissions

6) Data preparation: The final step in the data stage involves preparation or curation of the data. This step involves actions like:
• Adding, deleting or modifying the database
• Correcting errors and omissions in the data
• Making the data machine readable

→ Stage 3: Modelling
Data modelling is the third stage of the AI project cycle. Data modelling refers to selecting the specific algorithms and building models for working with the data.

7) Model selection:
Modelling involves selection of some form of AI data model for solving the problems and achieving goal. The selection of the model basically depends on the problem and the data being used. There are different models like neural networks and decision trees.

→ Stage 4: Evaluation and Deployment
The final stage of the AI project cycle involves evaluation, i.e. testing of the system, and the system deployment. This stage is further divided into:

8) Project evaluation: This step involves testing the system to see how the system responds to the data, check if there are any errors, see if the system is meeting the established goals etc. This stage will also involve making modifications of the project for ensuring that it functions as it was originally intended to function. The testing here will also correct the problem of overfitting. In overfitting, the AI system performs well with the past data but fails to deal with anticipated future data.

9) Project deployment: The AI projects are software or software and hardware projects which are undertaken for solving a problem or addressing a concern. This means that they have to be deployed for making them affective. Project deployment is done after the project clears the testing and evaluation stage. Most of the IT projects will end with the deployment stage. But in case of AI projects this will be followed by System Tuning stage.

10) System tuning: In AI projects the project does not end with deployment. The AI systems might need re-training and re-tuning at regular intervals for ensuring their accuracy and effectiveness. System tuning is also required for ensuring that AI system remains unbiased.
After action

11) Project review: This is nothing more than evaluating the project to gain learning from it. The project review also helps in separating the code for future use and finding out the areas for training and development of the team. It is essential to conduct project reviews after the completion of every project (both AI and non-AI).

6 comments:

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    ReplyDelete
    Replies
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  3. Anonymous31 July, 2021

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