AI project cycle

 The 5 key steps of the AI project cycle are Problem Scoping, Data Acquisition, Data Exploration, Modeling, and Evaluation, which guide building AI solutions by defining the problem, gathering and understanding data, creating algorithms, and testing their effectiveness, often leading to deployment. 

1. Problem Scoping: Clearly define the problem you're trying to solve, identify project goals, and understand constraints.
2. Data Acquisition: Collect accurate, reliable, and relevant data from various sources to train your AI model.
3. Data Exploration: Analyze and visualize the collected data, arrange it uniformly, and identify patterns or issues before modeling.
4. Modeling: Select and build AI models (e.g., using machine learning algorithms) using the prepared data to find solutions.
5. Evaluation: Test the performance of the developed models to ensure they meet the defined goals, checking for accuracy and effectiveness. 

4Ws Problem canvas

4Ws Problem Canvas  is a simple yet powerful framework for defining and understanding any problem by asking four core questions: Who is involved/affected, What the problem actually is, Where it occurs (context/location), and Why it's important to solve.