AI Engineering for Artificial Intelligence and Machine Learning

This document is a technical report that outlines the emerging discipline of AI engineering, which focuses on the application of tools, systems, and processes to enhance the effectiveness of AI in various real-world contexts. It details the unique characteristics of AI workloads compared to traditional applications, emphasizing the complexity involved in deploying AI on conventional IT infrastructure. The report describes how AI applications differ in their operational requirements, including the need for large volumes of diverse data and the challenges posed by model evolution and data quality. It presents a framework for operationalizing AI development, highlighting the importance of adopting agile methodologies such as DevOps, MLOps, and DataOps. These methodologies aim to streamline the transition of AI models into production and ensure effective management of data flows. The report concludes by recommending the establishment of centralized AI infrastructure to support these practices and facilitate the integration of AI into organizational processes.