Machine learning (ML) and artificial intelligence (AI) are often conflated, yet they represent distinct concepts within the realm of intelligent systems. AI is the overarching discipline focused on creating machines that can perform tasks requiring human-like intelligence, encompassing technologies such as rule-based programs and deep learning models. In contrast, ML is a subset of AI that emphasizes developing algorithms allowing computers to learn from data and make predictions without explicit programming. The synergy between AI and ML enables the development of complex systems, such as autonomous vehicles and conversational agents, where ML handles data pattern recognition and AI frameworks manage decision-making processes. Continuous Integration and Continuous Deployment (CI/CD) pipelines are pivotal in AI and ML development, offering automation in testing, collaboration, and deployment processes, which are crucial due to the experimental nature of these technologies. CI/CD ensures model reproducibility, handles probabilistic testing, and maintains environment consistency, thereby enhancing the efficiency and reliability of AI and ML applications.