Operationalizing Intelligence: A Unified Approach to MLOps and Scalable AI Workflows in Hybrid Cloud Environments

Abstract

Artificial intelligence (AI) is poised to play an increasingly significant role in most organizations, enabling them to offer innovative new products and services, delivering value through automation, and maximizing returns through advanced predictive and prescriptive insights. However, deploying and operating AI at scale is often hindered by complexity, confusion, and operational inertia. Multiple definitions and perspectives of MLOps and other related concepts, such as DataOps, DevSecOps, GitOps, AI Engineering, and AI Lifecycle Management, have created a patchwork of standards and best practices, focused on only certain aspects of the broader challenge of developing and operationalizing AI capabilities. This has made it difficult for enterprise decision-makers to understand the complexity of AI operations, how different roles and teams fit together, and how to establish company-wide systems and processes to manage the development and deployment of AI technologies at scale. As organizations enter into the next phase of AI maturity, these challenges need to be addressed, so that the initial experimentation with pilot AI projects can be scaled into large-scale production-grade AI systems that deliver the benefits of AI capabilities to enterprises more efficiently and effectively. In this chapter, we first provide an overview of AI and its business value. The overview is followed by a high-level look into the machine learning (ML) lifecycle, where we introduce the concept of operationalizing intelligence, AI system parts, and the need for AI-enabled business infrastructures, before delving into the operationalizing of the ML lifecycle with MLOps. Next, we highlight key themes that form the basis of the subsequent chapters in this book. We then introduce the audience and structure of the book. Finally, we conclude with a summary that recaps the key lessons shared in this chapter. This helps set the foundation for a deep-dive exploration of the various aspects of MLOps, as an instantiation of the broader concept of operationalizing intelligence, in the rest of the book.