This article provides a comprehensive overview of machine learning (ML) and deep learning (DL), two key technologies driving intelligent systems that offer artificial intelligence capabilities. ML enables systems to learn from specific training data, automate analytical model building, and solve tasks, while DL, a subset of ML, leverages artificial neural networks to outperform shallow ML models and traditional data analysis approaches in many applications. The authors distinguish between relevant terms and concepts, explain the process of automated analytical model building through ML and DL, and discuss challenges in implementing these systems in electronic markets and networked business. Key challenges include managing the triangle of architecture, hyperparameters, and training data, addressing bias and drift in data, ensuring explainability of predictions, and dealing with resource limitations. The article also highlights the emergence of AI as a service (AaaS) markets, which offer pre-trained models for specific tasks, and emphasizes the need for structured methodological guidance to build and assess analytical models effectively.This article provides a comprehensive overview of machine learning (ML) and deep learning (DL), two key technologies driving intelligent systems that offer artificial intelligence capabilities. ML enables systems to learn from specific training data, automate analytical model building, and solve tasks, while DL, a subset of ML, leverages artificial neural networks to outperform shallow ML models and traditional data analysis approaches in many applications. The authors distinguish between relevant terms and concepts, explain the process of automated analytical model building through ML and DL, and discuss challenges in implementing these systems in electronic markets and networked business. Key challenges include managing the triangle of architecture, hyperparameters, and training data, addressing bias and drift in data, ensuring explainability of predictions, and dealing with resource limitations. The article also highlights the emergence of AI as a service (AaaS) markets, which offer pre-trained models for specific tasks, and emphasizes the need for structured methodological guidance to build and assess analytical models effectively.