Optimization Tools and Techniques for AI and ML
Lingala Thirupathi, B. Vasundara, Pallavi Rajan, Tejaswi Arella · Advances in systems analysis, software engineering, and high performance computing book series · 2025
Artificial intelligence (AI) and machine learning (ML) have revolutionized various industries and domains, enabling data-driven decision-making and automated problem-solving. However, the optimization of AI and ML models remains a critical challenge, as it directly impacts their performance, accuracy, and efficiency. It explores the optimization tools and techniques employed in AI and ML, focusing on their current state, limitations, and future directions. Through a comprehensive literature review, and analysis of industry practices, this chapter delves into the optimization landscape, covering topics such as hyperparameter tuning, model compression, federated learning, and quantum computing. Additionally, it investigates emerging trends and cutting-edge techniques that hold the potential to shape the future of AI and ML optimization. By examining the synergies between optimization methodologies and AI/ML algorithms, this research aims to provide a holistic understanding of the optimization landscape and its implications for advancing AI and ML capabilities.