Genai And Predictive Ai Architecture

Explore The Generative AI & Predictive AI Architectures, Core Components, Layers, Model Types & Use Cases

What you'll learn

Foundations of Generative AI & Predictive AI

Understand the key components and layers within Generative AI and Predictive AI architectures.

Learn the differences between traditional AI, Generative AI, and Predictive AI and how to select the right approach.

Analyze enterprise AI architecture and its application

Explore various types of Generative AI models, including GANs, VAEs, Transformers, and Diffusion models.

Discover the best practices for using Generative AI in business applications.

Compare Conversational AI vs. Generative AI to understand chatbot and AI assistant implementations.

Explore over 40+ real-world use cases of Generative AI across industries.

Evaluate the top Generative AI tools and platforms, including OpenAI’s GPT models, Google’s Bard, Stability AI, and more.

Learn about popular Generative AI models and their key differentiators.

Understand the differences between Large Language Models (LLMs) and Generative AI and when to use each.

Learn the layers of Predictive AI architecture and how data is processed for forecasting and decision-making.

Explore various Predictive AI models, such as regression models, decision trees, neural networks, and time series forecasting.

Understand how Predictive AI works, including data ingestion, model training, and prediction generation.

Gain knowledge on how to implement Predictive AI in an organization

Compare Generative AI vs. Predictive AI applications to determine their respective strengths in different scenarios.

Explore the Generative AI Monitoring Architecture

Learn how Predictive AI Monitoring Architecture works to enhance model accuracy, reduce drift, and optimize decision-making.

Requirements

Basic Knowledge of AI

No advanced programming knowledge is required

Description

The rapid advancements in artificial intelligence (AI) have led to the rise of two transformative branches: Generative AI and Predictive AI. This comprehensive course explores their architectural foundations, key components, and practical applications in enterprise environments. Designed for AI professionals, data scientists, and business leaders, this course provides a deep dive into how these two AI paradigms work, their unique advantages, and their role in shaping the future of automation and decision-making.The course begins with an in-depth exploration of Generative AI Architecture & Key Components, where learners will understand the essential layers within Generative AI and how various models, such as GANs, VAEs, and diffusion models, generate new content. We will examine Types of Generative AI Models and their outputs, followed by discussions on best practices for leveraging Generative AI effectively in different domains. A comparative analysis of Traditional AI vs. Generative AI and Conversational AI vs. Generative AI will provide clarity on when to adopt these technologies. Enterprise implementation strategies will be covered in Enterprise Generative AI Architecture Layers & Components, along with real-world examples of Top 40+ Generative AI Use Cases and the Top 7 Most Popular Generative AI Tools and Platforms.Moving to Predictive AI, the course explores Predictive AI Architecture, including its layers and models, and delves into how Predictive AI works in real-world applications. We will discuss differences in architecture, purpose, and implementation compared to Generative AI, helping professionals make informed decisions when deploying AI solutions. Practical sessions on implementing Predictive AI in organizations will guide learners through real-world case studies.Finally, the course examines AI monitoring frameworks, focusing on Generative AI Monitoring Architecture and Predictive AI Monitoring Architecture to ensure AI systems remain efficient, ethical, and reliable. By the end of this course, participants will have a robust understanding of how to choose between Large Language Models (LLMs) and Generative AI, as well as the fundamental distinctions between Generative AI and Predictive AI applications.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Generative Al

Lecture 2 Generative Al Architecture & Key Components

Lecture 3 Types of Generative Al Models

Lecture 4 Layers Within Architecture of Generative Al

Lecture 5 How Generative Al Works

Lecture 6 What Are the Outputs of Generative Al

Lecture 7 Best Practices for Using Generative Al

Lecture 8 Traditional Al vs Generative Al

Lecture 9 How to Choose Between Al and GenAl

Lecture 10 Conversational Al vs Generative Al

Lecture 11 Enterprise Generative Al Architecture Layers & Components

Lecture 12 Top 40+ GenAl Use Case

Lecture 13 Top 7 Most Popular Generative AI Tools and Platforms

Lecture 14 Examples of Popular Generative AI Models

Lecture 15 Key Differences Between Large Language Models (LLMs) and Generative AI

Lecture 16 How to Choose Between Large Language Models (LLMs) and Generative AI?

Section 3: Predictive Al

Lecture 17 Predictive Al Architecture

Lecture 18 Layers in Predictive Al Architecture

Lecture 19 Models in Predictive Al

Lecture 20 How Predictive Al Works

Lecture 21 Implementing Predictive Al in an Organization

Section 4: Gen AI & Predictive AI -Differences in Architecture, Purpose, and Implementation

Lecture 22 Differences in Architecture, Purpose, and Implementation

Lecture 23 Generative AI vs Predictive AI: Applications

Section 5: Monitoring Architecture -Generative AI & Predictive AI

Lecture 24 Generative Al Monitoring Architecture

Lecture 25 Predictive Al Monitoring Architecture

AI & Machine Learning Professionals,Data Scientists & Analysts,AI engineers, data scientists, and ML practitioners,AI developers,Data analysts,C-level executives, AI strategists, and product managers,Innovation leaders seeking to integrate Generative AI and Predictive AI into business models,Professionals responsible for AI adoption and governance within enterprises.,Software Engineers & AI Developers,Academic Researchers & AI Enthusiasts