Overview
This course offers a comprehensive journey into architecting scalable and efficient Generative AI (GenAI) applications. It equips you with the skills to design, deploy, and optimize GenAI systems. The course starts by laying the foundational knowledge of GenAI, including its evolution from traditional AI to modern architectures, and dives deep into core concepts such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). By exploring these models, you’ll understand their vital role in enabling cutting-edge large language models (LLMs). As you progress, you'll delve into the LGPL architecture, breaking down its components—Gates, Pipes, and Loops—through hands-on simulations. This segment helps you grasp how these elements work in synergy to build robust GenAI applications. You'll also be introduced to best practices for building scalable systems, including containerization, load balancing, fault tolerance, and cloud-native deployment strategies. Practical lessons in infrastructure selection and deployment strategies provide a clear path toward real-world application. The course continues with a focus on building resilient GenAI applications, with essential topics like error handling, logging, monitoring, and high availability. You'll explore advanced security concerns, disaster recovery strategies, and cost optimization techniques for building GenAI systems that are both cost-effective and highly available. With case studies and hands-on examples, you’ll learn to apply these concepts in real-world scenarios like real-time trading systems and diagnostic recommendation systems. This course is ideal for professionals in AI, cloud computing, and software development who want to master the intricacies of building scalable and resilient GenAI systems. The course requires a fundamental understanding of AI concepts and programming, making it suitable for intermediate-level learners aiming to advance their skills in architecting AI-driven applications.
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