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The AI Project Cycle
The AI Project Cycle is a cycle/order of an AI Project which defines every step an organization must take to harness/get value (Monetary or others) from that AI Project to get more ROI (Return on Investment).
You might have seen AI Project Cycle images Starting from ‘Problem Scoping’, ignoring ‘Problem Identification’, But in this article we will discuss about the one with ‘Problem Identification’ which is a more accurate representation.
In Today’s Article, we will discuss the various stages of the AI Project Cycle, starting with Problem Identification, followed by Problem Scoping, Data Acquisition, Data Exploration, Data Modelling, Evaluation and finally Deployment.
Problem Identification: Problem Identification consists of:
- Clear identification of the problem, finding the root cause of the problem.
- Identify the ‘true’, underlying problem.
- Framing the Problem Correctly. (Framing is a Structural Representation of a Problem or an Issue, it involves explaining and describing the context of the problem to facilitate better understanding).
The Problem isn’t always clear. You may think that the Tip of the Iceberg is the problem, but in most cases, it's not. In many cases, the problems are not obvious, the problem may look small, but digging deep and down into the problem, we will realize that the problem has a lot to it, and that the beginning is nothing.
Problem Scoping: Whenever we are starting any work, certain problems always associated with the work or process. These problems can be small or big, sometimes we ignore them, sometimes we need…
