Architecting and Integrating Scalable AI Systems

Architecting and Integrating Scalable AI Systems focuses on designing end-to-end AI architectures that support scalable, reliable machine learning applications. In this course, you will learn how to translate business requirements into AI system designs and integrate machine learning models into production environments. You will begin by exploring system architecture concepts used to design AI systems, including requirements analysis, component design, and system modeling techniques. Next, you will learn how to deploy and optimize AI workloads in cloud environments while balancing performance, scalability, and operational costs. The course also covers designing scalable system components that support machine learning services and creating architecture diagrams that guide implementation. Finally, you will explore strategies for integrating AI services using APIs, messaging systems, and monitoring tools to ensure reliable system performance. By the end of this course, you will be able to design scalable AI architectures, integrate machine learning services into larger systems, and evaluate system performance and reliability in production environments. Tools and technologies covered include cloud computing platforms, REST APIs, system architecture frameworks, monitoring tools, and distributed system integration techniques

• Design scalable AI system architectures
• Deploy and optimize AI workloads in cloud environments
• Create system components and architecture diagrams
• Integrate AI services via APIs and distributed communication

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work