Generative AI Application Integration Patterns

Overview

This course explores how to effectively integrate Generative AI into application development, covering key concepts, practical strategies, and real-world applications. You'll gain insights into designing, deploying, and managing AI-driven solutions with a focus on ethical and responsible practices. By completing this course, you’ll develop skills to integrate large language models like GPT into your applications. You'll learn how to design scalable, AI-powered solutions, implement fine-tuning, and address real-world challenges. What sets this course apart is its blend of theoretical concepts and hands-on, practical applications. You'll work with the latest tools and frameworks to deploy AI models responsibly in production environments. This course is ideal for experienced developers, software architects, and technical product managers who are familiar with AI/ML and software development principles. It's geared toward those who wish to expand their expertise in AI integration.

Syllabus

Introduction to Generative AI Patterns

In this section, we explore generative AI integration patterns, transformer and diffusion model architectures, and responsible experimentation strategies to enable practical AI applications.

Identifying Generative AI Use Cases

In this section, we explore methods to identify generative AI (GenAI) use cases using interaction frameworks, analyze business value, and differentiate between comprehensive and generative applications.

Designing Patterns for Interacting with Generative AI

In this section, we cover integrating GenAI into workflows, focusing on real-time interactions, prompt processing, and model monitoring for continuous improvement.

Generative AI Batch and Real-Time Integration Patterns

In this section, we explore batch and real-time integration patterns for LLMs, focusing on throughput, latency, and pipeline design for practical system implementation.

Integration Pattern: Batch Metadata Extraction

In this section, we explain how to extract metadata from 10-K reports using GenAI and cloud tools.

Integration Pattern: Batch Summarization

In this section, we explore Generative AI for financial document summarization and cloud integration.

Integration Pattern: Real-Time Intent Classification

In this section, we cover real-time intent classification and AI integration for fast user interactions.

Integration Pattern: Real-Time Retrieval Augmented Generation

In this section, we cover RAG systems for financial services, including use cases, architecture, and implementation.

Operationalizing Generative AI Integration Patterns

In this section, we explore operationalizing GenAI integration patterns with a focus on scalability and compliance.

Embedding Responsible AI into Your GenAI Applications

In this section, we examine responsible AI practices for GenAI applications, focusing on fairness, interpretability, privacy, and security to ensure ethical and compliant AI systems.