Managing the AI Lifecycle in Azure

Organizations are rapidly adopting artificial intelligence, but deploying a model is only the first step. To deliver real business value, you must understand how to manage, monitor, and maintain AI systems throughout their entire operational life. This text-only course guides you through the foundational concepts of the AI lifecycle in Azure, helping you transition from basic model building to sustainable production deployments. You will learn how to oversee every phase of an AI project, ensuring compliance, performance, and reliability using Azure tools. By focusing on modern MLOps (Machine Learning Operations) and responsible AI principles, you will gain the skills needed to keep your models accurate and secure over time. What you'll learn: - Understand the core phases of the AI lifecycle from data preparation to deployment - Configure Azure Machine Learning workspaces and manage cloud-based computational resources - Implement MLOps principles to automate model training, testing, and deployment pipelines - Monitor production models for data drift, performance degradation, and system latency - Apply responsible AI practices, including fairness evaluation and model interpretability - Manage model versioning, lineage, and metadata tracking within Azure We begin with essential terminology and the conceptual framework of AI governance, establishing a solid foundation. Next, you will explore step-by-step written guides on setting up environments, deploying models, and establishing automated monitoring systems. Each concept is reinforced with practical scenarios and configuration examples. This course is designed for beginners, developers, and project managers looking to understand AI operations in the cloud. No prior experience with Azure or machine learning deployment is required. Start your journey toward managing reliable, production-ready AI systems in Azure today.