Introduction

Welcome to Generative AI in Practice: Advanced Insights and Operations. I’m Dr. Mohsen Amiribesheli, and I’m thrilled to guide you through the exciting realm of Generative AI. This lesson not only explains theoretical concepts but also demonstrates how to implement them in real-world applications.

For years, I have navigated the rapidly evolving AI landscape and understand the challenges of moving from theory and proof-of-concept (POC) to full-scale implementation. With a current shortage of hands-on expertise in Generative AI, this course is designed to provide you with practical skills that drive innovation.

Since you already have a solid background in basic AI concepts and programming, we can dive straight into the advanced topics. Unlike traditional lecture-based courses, our dynamic and interactive sessions will mirror the multifaceted challenges encountered in real AI and Generative AI projects.

Evolution of AI Models

In this lesson, we start with a historical journey through AI—from traditional rule-based systems to state-of-the-art generative technologies driven by transformers and multimodal architectures. This perspective lays a solid foundation, highlighting how AI has evolved into a versatile tool for decision making.

Next, we’ll explore the intricate architectures of large language models, including transformers and self-attention mechanisms. We will cover critical aspects such as tokenization, embedding processes, and the fine-tuning of hyperparameters.

Practical Implementation

In our hands-on session, you will learn how to operationalize AI models in an enterprise context using tools like Azure AI Studio. The interface below showcases a software tool for managing AI model deployments:

We will also discuss LLM Ops and data operations strategies, including Retrieval Augmented Generation (RAG). This method utilizes real-time data retrieval to strengthen model performance, addressing limitations in traditional approaches.

The next slide visualizes strategies to overcome common RAG challenges:

Advanced Integration and Applications

Finally, we explore how advanced stack integration, semantic matching, and dynamic embeddings can incorporate real-time data into AI systems. This approach enhances contextual responses across various sectors, including finance, healthcare, and customer service.

The curriculum outline below details our journey into practical Generative AI:

Let's embark on this transformative journey together and unlock the full potential of Generative AI in practice.