To successfully build and deploy AI solutions, the model development life cycle (MDLC) must be integrated with the software development life cycle (SDLC), as represented visually in Figure 4.1. In the fast-evolving world of technology, AI models and software systems must work seamlessly together to deliver optimal results. The MDLC encompasses problem definition, data collection, feature engineering, model training, and evaluation. At the same time, the SDLC includes phases like requirement analysis, system design, implementation, testing, deployment, and maintenance. Integrating these life cycles ensures that AI models are technically sound and embedded within robust software frameworks that can support their deployment and ongoing operation.
FIGURE 4.1Visual representation of the integration between the MDLC and the SDLC, illustrating how AI model development must be aligned with traditional software engineering practices for successful deployment of AI solutions
The need for this integration stems from the distinct but complementary nature of MDLC and SDLC. Whereas MDLC focuses on developing predictive models and algorithms, SDLC provides a structured approach to building and maintaining software applications. AI models might fail to meet business requirements or align with software system constraints without proper integration, ...
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