AI process lifecycle

This is the AI process lifecycle :

About the steps:

Problem Scoping :

Problem scoping is the first step in the AI project cycle. It means understanding what problem we want the AI to solve. In this step, we clearly define the goal, find out who will use the solution, and decide what data is needed. It helps us focus on the right problem so that the AI system can give useful and correct results.

Data Acquisition :

Data acquisition is the second step in the AI project cycle. It means collecting the right data needed to solve the problem. The data can come from surveys, sensors, the internet, or experiments. Good and accurate data helps the AI system learn better and give correct answers.

Data Exploration :

Data exploration is the third step in the AI project cycle. In this step, we study and understand the data we collected. We look for patterns, missing information, or mistakes in the data. This helps us know how the data can be used and what changes we might need before training the AI model.

Modeling :

Modeling is the fourth step in the AI project cycle. In this step, we use the prepared data to train the AI system. The computer learns from the data and builds a model that can make predictions or decisions. For example, it can learn to recognize animals, read handwriting, or suggest movies.

Evaluation :

Evaluation is the fifth step in the AI project cycle. In this step, we check how well the AI model is working. We test it with new data to see if it gives correct answers. If the results are not accurate, we make changes and train it again. This helps improve the AI system before it is used in real life.

Deployment :

Deployment is the final step in the AI project cycle. In this step, the trained and tested AI model is used in the real world. It helps people solve real problems, like suggesting songs, detecting pests, or guiding self-driving cars. After deployment, the AI system keeps getting updates to work even better over time.