Understanding software complexity in AI systems
Several distinct but interdependent types of software complexity exist in AI-enabled systems. Architecture serves as a tool to manage this complexity through modeling, which helps the architecture team reason about the system and provides a foundation for detailed specifications and team communication.
Figure 3.1: Software development lifecycle for AI systems
As shown in Figure 3.1, the development process for AI systems follows a structured lifecycle that begins with conceptual development, then moves through requirements gathering and architecture development before proceeding to design and development. After development, the process continues with integration, unit testing, system testing, and finally deployment. This systematic approach helps manage the inherent complexities of AI systems by ensuring that architectural considerations are addressed early in the development process. An excellent resource for learning about...
