In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), transitioning from theoretical models to robust, adaptive production systems remains a significant challenge in the digital age. As data-driven methodologies revolutionize problem solving across industries, a critical gap exists between research achievements and reliable real-world deployment. Key obstacles include concept drift and data quality issues that arise from unpredictable changes in data distributions and operational complexities that systematically affect model performance, reliability, and efficiency. This thesis addresses these challenges by introducing an overarching framework for adaptive ML systems that operate reliably in dynamic, real-world environments, incorporating innovative methodologies for dynamic drift detection and real-time data quality assessment bounded by robust Machine Learning Operations (MLOps) strategies. These integrated components enable the creation of production-grade ML systems that can efficiently adapt to shifts in data distributions and assess data quality in real-time, ensuring stable and reliable performance in dynamic environments. The proposed approaches are validated through real-world use cases, demonstrating significant improvements in predictive accuracy and operational efficiency. By deploying these adaptive systems in industrial contexts, the thesis highlights their potential to deliver reliable, high-performance ML solutions tailored to the demands of complex time-sensitive applications. This work offers concrete solutions for translating theoretical advances into practical applications, contributing to developing robust and scalable ML systems for real-world deployment.

In the era of AI-driven innovation, ensuring the reliability and adaptability of machine learning (ML) systems in dynamic environments remains a fundamental challenge. This thesis addresses key obstacles such as concept drift and data quality, which affect model performance in real-world applications. By integrating novel drift detection mechanisms, real-time data quality assessment, and robust MLOps strategies, this work proposes a comprehensive framework for self-adaptive ML systems. These solutions enable industrial ML deployments to maintain predictive accuracy, efficiency, and resilience against evolving data conditions. Through real-world case studies, this research demonstrates significant improvements in operational stability and decision-making, bridging the gap between theoretical advancements and practical implementation. By tackling these challenges, this thesis contributes to the development of scalable, trustworthy, and high-performance ML solutions tailored for industrial and mission-critical applications.