Page 29 - Ai V2.0 Flipbook C8
P. 29
02 STAGES OF AI
PROJECT CYCLE
Learning Outcomes
• Overview of Stages of AI Project Cycle • Problem Scoping
• Data Acquisition • Data Exploration
• Modelling • Evaluation—The Testing Stage
• Deployment
Hey AIVA! I was reading about the stages of the AI
project cycle, and it seems quite interesting.
That sounds exciting! What happens after that?
Yes, REVA, it is! The AI project cycle involves different
stages, and each one plays a crucial role in building a
successful AI model. After modelling, we move on to evaluation, where we test
the model's performance. Finally, the deployment stage
puts the model into real-world use.
Can you explain the stages to me? I’m a bit confused
about how it all comes together.
Wow, so there’s a clear step-by-step process.
Sure! First, there's the problem scoping stage, where you
define the problem you want to solve. After that, you move
to data acquisition to gather the relevant data. Exactly! Each stage builds on the previous one to create an
effective AI solution.
Okay, and what comes next?
Let’s learn more about how the Stages of AI Project Cycle
come together in practice.
Next, it's the data exploration stage. You need to clean
and analyse the data to understand its patterns. Then, we
proceed to modelling, where we build the AI model.
Stages of AI Project Cycle 27

