During which phase of the AI Development Life Cycle is model building emphasized?

The phase during which model building is emphasized is the Development phase. This phase is crucial because it focuses specifically on creating the AI model based on the defined requirements from the planning stage. During development, data scientists and engineers collaborate to select the appropriate algorithms, processes, and techniques to construct the model that will process data, learn patterns, and ultimately provide predictions or insights.

In this phase, aspects such as feature selection, data preprocessing, and algorithm training are executed, which are fundamental to ensuring that the model functions effectively and meets the outlined objectives. The emphasis is on iteratively refining the model and validating it against the training data to ensure accuracy and reliability before moving on to subsequent phases.

The other phases, while critical to the overall life cycle, focus on different aspects. Planning lays out the project goals and objectives. Implementation pertains to the coding and integration of the AI models into a larger system. Deployment involves putting the finalized model into a production environment where it can operate in real-world scenarios. Hence, model building is distinctly a core focus during the Development phase.