Architecting AI Solutions – Scalable GenAI Systems

Resumen

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.

Dive into the foundational knowledge of GenAI, exploring its evolution from traditional AI to modern architectures. Deepen your understanding of core concepts such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), crucial for the development of cutting-edge large language models (LLMs).

Progress into the LGPL architecture, where you'll explore the components of Gates, Pipes, and Loops through practical simulations.

This approach ensures a robust grasp of how these elements work in synergy to create efficient GenAI applications. The course introduces best practices for building scalable systems, focusing on containerization, load balancing, fault tolerance, and cloud-native deployment strategies.

Gain hands-on experience in infrastructure selection and deployment strategies for real-world application.

Continue to specialize in building resilient GenAI applications, covering topics like error handling, logging, monitoring, and ensuring high availability. Address advanced security concerns, disaster recovery strategies, and cost optimization techniques to create cost-effective, highly available GenAI systems.

Through case studies and hands-on examples, apply these concepts in real-world scenarios like real-time trading systems and diagnostic recommendation systems.

This course is specifically designed for professionals in AI, cloud computing, and software development keen on mastering scalable and resilient GenAI systems. With a fundamental understanding of AI concepts and programming required, it's suitable for intermediate-level learners looking to advance their skills in architecting AI-driven applications.

University:

n/a

Provider:

Coursera

Categories:

Cloud Computing Courses, Generative AI Courses, Docker Courses, Variational Autoencoders Courses, Containerization Courses, Fault Tolerance Courses

Materias