Engineering Scalable and Adaptive AI Systems: An MLOps-Driven Framework for Innovation in Intelligent Applications

The application of artificial intelligence (AI) systems is still a significant block in innovation-oriented businesses. This paper proposes a modular MLOps-based framework that can facilitate the scaling, flexibility, and repeatability of deployable machine learning models in industry. Combining ideas of modern software engineering and innovation systems theory, the framework includes DataOps, ModelOps, and CI/CD orchestration categories, whereas continuously validating data, tracking experiments, monitoring, and retraining processes happen automatically. The AI4I 2020 Predictive Maintenance dataset has been empirically validated, and deployment into real-life on customer habits modelling and cybersecurity anomaly detection. The model successfully resulted in superior classification accuracy (98.5%) and AUC (0.96), as well as a deployment time (40% decrease as compared to the baseline strategies). The drift tracking and retraining mechanisms showed they are prepared to react in time, although retraining was not initiated during the present assessment. In addition to technical performance, the framework allows cross-functional collaboration, accelerated iteration, and institutional learning, which are some of the central drivers of systemic innovation. The proposed study will present a replicable engineering model that can be adopted between AI experimentation and sustainable innovation practices within intelligent application development.