Architecting AI Solutions – Scalable GenAI Systems

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.

Syllabus

• Introduction
In this module, we will introduce the foundational concepts and prerequisites essential for mastering scalable GenAI systems. You’ll also gain clarity on the course structure, helping you understand the key components that will guide your learning journey.
• GenAI (Generative AI) Deep Dive
In this module, we will take an in-depth look at Generative AI (GenAI), understanding its foundational concepts and evolution. Through real-world applications and an exploration of VAEs and GANs, you’ll gain insight into the power and potential of GenAI systems.
• The LGPL Architecture - Deep Dive
In this module, we will dive deep into the LGPL architecture, exploring its vital components and layers. Through hands-on simulations, you will build practical understanding by integrating Gates, Pipes, and Loops into scalable GenAI systems.
• Building Scalable GenAI Applications
In this module, we will explore key infrastructure considerations for GenAI applications, diving into containerization, Docker, and architectural choices. You’ll also learn how to implement strategies like load balancing and fault tolerance to ensure your applications are scalable and resilient.
• Building for Cloud-Native Deployments
In this module, we will explore how cloud platforms enable scalable and efficient deployment of GenAI applications, helping you understand the key advantages of building for the cloud.
• Building Resilient GenAI Applications
In this module, we will focus on strategies for building resilient GenAI applications, from managing errors and exceptions to ensuring uptime with monitoring and alert systems. Learn how to maintain reliability and reduce failure impacts.
• Disaster Recovery and High Availability Strategies
In this module, we will cover strategies to ensure your GenAI applications are both resilient and high-performing, exploring disaster recovery techniques and high availability solutions, including cloud-based tools from AWS.
• Security Threats in GenAI Applications
In this module, we will examine common security threats facing GenAI applications and explore the role of Explainable AI (XAI) in safeguarding against adversarial risks, helping you secure your systems from emerging threats.
• Cost Optimization Strategies for GenAI Infrastructure
In this module, we will focus on cost optimization strategies, including resource right-sizing and containerization. You’ll learn how to balance performance and cost efficiency when deploying GenAI applications.
• Advanced Topics in GenAI Application Architecture
In this module, we will explore advanced concepts in GenAI architecture, such as Explainable AI, MLOps, and ethical AI. You’ll also discover the emerging trends that are shaping the future of GenAI systems.
• Next Steps
In this final module, we will provide guidance on how to continue your journey of mastering scalable GenAI systems, offering recommendations for next steps and further learning opportunities.

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Taught by

Packt - Course Instructors

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Subjects

Computer Science