Overview of the AI Project Cycle

Study Notes

Overview of the AI Project Cycle

An AI project typically follows six main stages:

Problem Understanding: Identify the issue you wish to solve; create a clear statement describing what you aim to achieve.
Data Collection: Collect relevant data to train the AI model; this includes crawling existing sources or creating original data collections.
Data Wrangling: Process and prepare the raw data so it can be used effectively.
Model Development, Training, Evaluation: Train a suitable machine learning algorithm on the prepared data.
Model Deployment and Maintenance: Ensure the trained model runs smoothly and produces accurate predictions in the intended application.
Monitoring and Updates: Continuously evaluate the effectiveness of the model, making changes as required.

These stages involve considerable effort and can vary in duration depending on factors like complexity and team size. Key activities within these steps include:

Problem Definition: Clearly stating the objectives of the project.
Preparation: Setting up an infrastructure supporting data storage and computation.
Exploration: Experimenting with various approaches to solving the problem.
Iteration: Testing various hypotheses until a satisfactory solution is found.

Each stage is crucial, and mistakes at one point could have cascading effects throughout the project. Therefore, care must be taken with each aspect, ensuring that the project moves forward efficiently.

Description

Learn about the six main stages of an AI project cycle, from problem understanding to monitoring and updates. Explore key activities within each step and the importance of careful consideration at every stage.