| Abstract: | This comprehensive course dives into the intricacies of designing scalable Generative AI (GenAI) systems. Starting with the evolution of AI architectures, you'll explore how GenAI technologies like Variational Autoencoders and Generative Adversarial Networks have transformed the field. Real-world applications are highlighted to provide context and inspiration, setting the stage for understanding the challenges and opportunities in building GenAI applications. The course delves into the LGPL architecture, a robust framework for creating scalable and resilient systems. Through hands-on exercises, you'll simulate key components like gates and feedback loops, gaining practical expertise in implementing advanced architecture layers. The curriculum extends to cloud-native deployment strategies, microservices, load balancing, and fault tolerance, ensuring your applications are ready for enterprise-scale demands. Advanced topics include security, monitoring, and ethical considerations for responsible AI development. You'll explore real-world case studies, tackling challenges such as disaster recovery, high availability, and cost optimization. By the end of the program, you'll be equipped to build, deploy, and manage GenAI systems that are efficient, secure, and future-proof, ready to meet the demands of modern AI applications. To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub. What you will learn Design scalable GenAI systems with advanced architectural techniques Implement cloud-native solutions using microservices and containers Develop resilient GenAI systems with robust error handling strategies Apply security measures to prevent threats and safeguard AI models Optimize GenAI infrastructure for cost efficiency and performance gains Build end-to-end AI applications with real-world use case simulations Audience This course is designed for experienced developers, architects, and AI practitioners looking to deepen their expertise in scalable Generative AI systems. Participants should have foundational knowledge of AI concepts, programming (preferably Python), and basic cloud computing principles. About the Author Paulo Dichone: Paulo Dichone, a dedicated developer and educator in Android, Java, and Flutter, has empowered over 80,000 students globally with both soft and technical skills through his platform, Build Apps with Paulo. Holding a Computer Science degree and with extensive experience in mobile and web development, Paulo's passion lies in guiding learners to become proficient developers. Beyond his 5 years of online teaching, he cherishes family time, music, and travel, aiming to make impactful developers irrespective of their background. |
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