Software Engineering in the Era of AI and Data Science

Software Engineering in the Era of AI and Data Science offers a comprehensive examination of modern software development practices shaped by artificial intelligence and data-driven methodologies. The book begins with the fundamentals of software engineering, including requirements engineering, system design, coding practices, testing, and maintenance.
It then explores how AI and data science influence each stage of the software development lifecycle. Topics include data engineering pipelines, machine learning model development, integration of AI components into software systems, and deployment strategies using cloud and containerized environments. The book also introduces MLOps practices for managing the lifecycle of machine learning models in production.
A strong emphasis is placed on quality assurance, model validation, monitoring, and handling model drift in dynamic environments. The text further discusses ethical considerations such as bias, fairness, transparency, and accountability in AI-driven systems.