Master Generative Ai: Professional Level Llm Application Dev

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Master Generative Ai: Professional Level Llm Application Dev - AD-TEAM - 01-18-2025

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Master Generative Ai: Professional Level Llm Application Dev
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 15.49 GB | Duration: 16h 55m

Master Generative AI: Building Professional-Grade LLM Applications for Advanced Solutions

What you'll learn

Foundations of Generative AI: Understand the core concepts, architectures, and workflows involved in building Generative AI applications.

Working with Large Language Models (LLMs): Explore leading LLMs like GPT-4, Llama, and more, and learn how to integrate them into real-world applications.

Retrieval-Augmented Generation (RAG): Learn RAG concepts, components, and implementation techniques to create efficient AI systems.

LangChain Mastery: Gain hands-on experience with LangChain, including prompt templates, chains, memory management, and advanced RAG capabilities.

Cloud AI Platforms: Work with tools like AWS Bedrock, Google Vertex AI, and other platforms for fine-tuning and deploying AI solutions.

AI Application Development: Build professional-grade applications such as chatbots, sentiment analysis tools, and knowledge systems using advanced frameworks.

Multimodal AI Applications: Learn to implement AI systems that integrate multiple data types, including text, images, and structured data.

LLMOps & Deployment: Understand LLMOps, optimization techniques, and deployment strategies for creating scalable, production-ready AI systems.

Requirements

Basic Programming Knowledge: Familiarity with programming languages like Python is essential for implementing Generative AI applications.

Understanding of AI/ML Basics: A foundational understanding of artificial intelligence and machine learning concepts will be helpful.

Experience with APIs: Basic experience working with APIs will assist in integrating Large Language Models and other tools.

Familiarity with Cloud Platforms: Some prior exposure to cloud platforms like AWS, Google Cloud, or Azure is beneficial but not mandatory.

Basic Understanding of Data Structures: Knowledge of data organization concepts will help in managing AI workflows effectively.

Command Line Tools: Basic knowledge of using the command line for setup and troubleshooting is recommended.

Eagerness to Learn: A curious mindset and willingness to explore complex systems and workflows are critical for success.

Reliable Internet Connection: Access to a stable internet connection for cloud platform access and hands-on exercises.

Description

Master the art of building professional-grade Generative AI applications with this comprehensive course designed for advanced developers, data scientists, AI enthusiasts, and technology leaders. This program covers everything you need to know about leveraging Large Language Models (LLMs) to create robust, scalable, and production-ready AI-powered solutions. Whether you're looking to enhance your skills or build innovative applications, this course is your gateway to success in the AI-driven future.Start with an in-depth exploration of foundational concepts, including the architecture of Generative AI systems, key components, and tools. Learn about advanced topics such as Retrieval-Augmented Generation (RAG), LangChain, LlamaIndex, and the integration of cutting-edge orchestration frameworks. Gain hands-on experience with cloud platforms like AWS Bedrock, Google Vertex AI, and others to fine-tune your applications and deploy them in real-world scenarios.This course also delves into practical implementations, including chatbots with memory, advanced data retrieval, sentiment analysis tools, and multimodal AI applications. You'll master essential techniques like managing custom data, creating efficient pipelines, and optimizing performance for scalability. By the end of the course, you'll have the expertise to design, deploy, and maintain production-level AI systems that exceed professional standards, empowering you to lead in the rapidly evolving field of Generative AI development and innovation.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Prerequisite Learning Resouces

Lecture 3 Basic Architecture Overview for Gen AI Applications

Lecture 4 Advanced Gen AI Application Architectures

Lecture 5 Multi-Level Architecture Exploration (Level 1, Level 2, Level 3)

Lecture 6 Preview of a Professional Gen AI Application

Section 2: Advanced Gen AI Application Architecture

Lecture 7 Selecting the Right Foundation LLMs

Lecture 8 Comprehensive Tool Stack for Gen AI Applications

Lecture 9 Orchestration Frameworks for Scalable Solutions

Section 3: Retrieval-Augmented Generation (RAG) Technique

Lecture 10 Introduction to RAG and Key Concepts

Lecture 11 Important Concepts of RAG

Lecture 12 Core Components of RAG

Lecture 13 Addressing RAG Implementation Challenges

Section 4: Choosing Orchestration Frameworks for Application Development

Lecture 14 Choosing Orchestration Frameworks for Application Development

Section 5: LangChain - A Modern Framework for LLM Integration

Lecture 15 Overview of LangChain, Evolution, and Learning Path

Lecture 16 LangChain Basics: Connecting with Leading LLMs (OpenAI's GPT-4, GPT-4o Mini, and

Lecture 17 Prompt Templates for Integrating Logic into LLM Interactions

Lecture 18 Chains for Sequencing Instructions

Lecture 19 Output Parsers for Response Formatting

Lecture 20 Working with Custom Data (Data Loaders) & RAG Basic Concepts

Lecture 21 Different RAG Components like ( Splitters, Embeddings, Vector Stores, Retrievers

Lecture 22 Basic RAG Implementation with LCEL

Lecture 23 Memory Management in LangChain: Temporary and Permanent Memory

Section 6: LangChain Expression Language (LCEL)

Lecture 24 Introduction to Langchain Expression Language (LCEL) | Chains and Runnables

Lecture 25 Built-in Runnables in LCEL

Lecture 26 Built-in Functions in runnables

Lecture 27 Combining LCEL Chains

Lecture 28 RAG demo with LCEL

Section 7: LangChain Ecosystem

Lecture 29 Comprehensive Overview of the LangChain Ecosystem

Lecture 30 LangServe Demo

Lecture 31 LangGraph Demo

Lecture 32 LangSmith Demo

Section 8: Mastering Prompt Engineering

Lecture 33 Prompt Engineering

Section 9: Level 1 Application Development

Lecture 34 Introduction to Level 1 Application

Lecture 35 Advanced Chatbot with Memory

Lecture 36 Key Data Extraction

Lecture 37 Sentiment Analysis Tool

Lecture 38 SQL-based Question Answering Application

Lecture 39 PDF-based Question Answering

Lecture 40 Basic Retriever Applications

Lecture 41 RAG Application

Section 10: LlamaIndex - An Alternative of LangChain

Lecture 42 Introduction to LlamaIndex

Lecture 43 In-depth Exploration of LlamaIndex

Section 11: LLMOps - AI Operations for Gen AI Applications

Lecture 44 Introduction to LLMOps

Lecture 45 Key Challenges

Lecture 46 Generative AI with Google Cloud (Vertex AI) a LLMOps Platform

Lecture 47 Vertex AI Hands-On on Google Cloud

Lecture 48 Vertex AI Local Setup - Run Gemini Pro on Local Machine

Lecture 49 RAG on Vertex AI with Vector Search and Gemini Pro

Lecture 50 LLM powered application on Vertex AI

Lecture 51 Fine tuning Foundation Model VertexAI

Lecture 52 Introduction to AWS Bedrock

Lecture 53 Hands-on AWS Bedrock

Lecture 54 End to End RAG using AWS Bedrock

Section 12: Level 2 Application Development

Lecture 55 Introduction to Level 2 Application

Lecture 56 Application for Converting Slang to Formal English

Lecture 57 Blog Post Generation Application

Lecture 58 Text Summarization with Split

Lecture 59 Text Summarization Tools

Lecture 60 Key Data Extraction from Product Reviews

Lecture 61 Interview Questions Creator Application

Lecture 62 Medical Chatbot Project

Lecture 63 Level 2 Application Deployment

Section 13: Multimodal Gen AI Applications

Lecture 64 Overview of Multimodal LLM Applications

Lecture 65 Steps to implement Multimodal LLM Applications

Lecture 66 Building Multimodal LLM Applications with LangChain & GPT 4o Vision

Section 14: Agent & Multi-Agent Applications

Lecture 67 Introduction to AI Agents and Agentic Behaviors

Lecture 68 Multi-Agent Development with CrewAI

Section 15: Level 3 (Professional) Application Development

Lecture 69 Introduction to Level 3 Application

Lecture 70 Project 1: Advanced RAG-Based Knowledge Management System

Lecture 71 Project 2: Medical Diagnostics Support Application

Section 16: Deploying Gen AI Applications with CI/CD for Production

Lecture 72 Production-Grade Deployment on AWS

AI Enthusiasts and Developers: Those who want to deepen their understanding of Generative AI and learn how to build professional-grade applications using Large Language Models (LLMs).,Machine Learning Engineers: Professionals looking to enhance their skills in AI application development, particularly in the areas of RAG, LangChain, and LLMOps.,Data Scientists: Individuals aiming to apply advanced AI techniques to solve complex data-related problems, including building intelligent systems and automation.,Cloud Engineers: Developers and engineers interested in using cloud platforms like AWS and Google Cloud to deploy AI applications and fine-tune LLMs.,Software Engineers: Those interested in expanding their expertise to include the integration of advanced AI models into software products.,Tech Entrepreneurs & Innovators: Individuals looking to create AI-powered solutions and products that can disrupt industries or solve real-world problems.,Students & Researchers: Those seeking hands-on experience with cutting-edge AI technologies and frameworks to pursue careers or further studies in AI.,Anyone Interested in Future-Proofing Skills: Individuals eager to stay ahead in the rapidly evolving AI and machine learning fields.

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