Generative AI Specialization course
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Generative AI Specialization course

Instructor: SaratahKumar c Language: ENGLISH

About the course

The Generative AI Specialization Course is an industry-focused, hands-on training designed to help professionals master Generative AI, LLMs, and cloud AI solutions. Covering text, image, and multimodal generation, it includes prompt engineering, RAG, fine-tuning, AI agents, and responsible AI practices.

Learners will gain practical experience with GPT, Claude, Gemini, LLaMA, Titan, Cohere, and Stable Diffusion, using Amazon Bedrock, Azure AI Foundry, Google AI Studio, and Hugging Face. This 90-hour live program features 50+ hands-on labs, 20+ projects, debugging sessions, and peer networking to ensure job-ready expertise in AI development.

Program features

About the Program

The Generative AI Specialization Course is an industry-focused, hands-on training designed to help professionals master Generative AI, LLMs, and cloud AI solutions. Covering text, image, and multimodal generation, it includes prompt engineering, RAG, fine-tuning, AI agents, and responsible AI practices.

Learners will gain practical experience with GPT, Claude, Gemini, LLaMA, Titan, Cohere, and Stable Diffusion, using Amazon Bedrock, Azure AI Foundry, Google AI Studio, and Hugging Face. This 90-hour live program features 50+ hands-on labs, 20+ projects, debugging sessions, and peer networking to ensure job-ready expertise in AI development.

Key Highlights 

  • 90 Hours of Live sessions from Industrial Experts 
  • 50+ Live Hands-on Labs 
  • 20 Real-time industrial projects 
  • One-on-One Debugging with Industry Mentors

Watch Demo

Who Can Apply for the Course?

  • AI & ML Engineers building generative AI
  • Data Scientists & Researchers exploring LLMs & RAG
  • Developers integrating AI into apps
  • Cloud & DevOps Engineers using AI services
  • AI Enthusiasts & Applied Scientists in generative AI
  • Tech Professionals working with GPT, Claude, LLaMA, Stable Diffusion
  • Startup Founders leveraging AI
  • Product Managers & Leaders exploring AI strategies
  • Anyone mastering LLMOps & AI tools

Program Curriculum

Module 1: Introduction to Generative AI
  • Introduction to Generative AI
  • Applications and impact of Generative AI
  • Evolution and Architecture of Generative AI
  • How does LLM work?
  • Different types of Generative AI
    • Text generation
    • Image generation
    • Audio and speech recognition
    • Multi-modality
  • Foundation Models vs LLMs
  • Embedding vs Image generation vs Text and Code generation
  • How to improve LLM results?
    • Prompt Engineering with Context
    • Retrieval Augmented Generation (RAG)
    • Fine-tuned model
    • Trained model
  • Vector databases for Generative AI
  • Introduction to LangChain
  • Choose the best foundation model for your needs
Module 2: Prompt Engineering Hands-on
  • Introduction to Prompt Engineering
  • How does tokenization work?
  • Necessity of Prompt Engineering
  • Basic Prompt Structure
  • Clear and direct instructions
  • Assigning Roles (Role Prompting)
  • Splitting Data from Instructions
  • Formatting Output using prompt
  • Step by step using Precognition
  • Using Examples in prompt
  • How to Avoid Hallucinations
  • Project:Building Complex Prompts (Industry Use Cases)
Module 3: Deep Dive into Generative AI on Cloud (AWS, Azure & GCP)
  • Choose from leading Foundation models
    • AI21: Jamba, Jurassic
    • Amazon: Titan Models
    • Anthropic: Claude Models
    • Cohere: Command Models
    • Meta: Llama
    • Mistral AI
    • Stability AI Models
    • Open AI Models
    • DeepSeek
    • Google Gemini models
    • Hugging Face models
  • Model Hyperparameter Configurations
  • AWS for Generative AI
    • Getting started with Amazon Bedrock
      • Experiment with Foundation models for different tasks
        • Chat/text playground
        • Image playground
      • Privately customize FMs with your data
      • Amazon Bedrock Converse API
      • Amazon Q – Generative AI Assistant
        • Amazon Q Business
        • Amazon Q Developer
      • Amazon SageMaker for Generative AI
        • SageMaker JumpStart pre-trained models
  • Mastering Google AI: From AI Studio to Gemini API
    • Google AI Studio Introduction
    • Gemini API Overview
    • Google AI Studio playground
      • AI Playground Chat Audio Docs & Images
      • Real-Time Streaming Audio & Video
    • GCP Vertex AI Generative AI Solutions
      • Model Garden Foundation Models
      • Vertex AI Studio Getting Started
        • AI Capabilities Chat Vision & Speech
        • Prompt Management Gallery & Optimization
        • Model Tuning & Customization
        • Vertex AI Vector Search
  • Azure for Generative AI: Azure AI Foundry
    • Getting started with Azure AI Foundry
    • Understanding RBAC Roles in Azure AI Foundry
    • Understanding Azure AI Foundry resources
      • AI project
      • AI hub
      • AI Services
      • Azure OpenAI Service
    • Azure AI Model Catalog Discover and Deploy AI Models
    • AI Playground Experiment, Customize & Build
    • Azure AI Agent Secure & Scalable Enterprise Automation
    • Fine-Tune AI Models with Your Data
    • Prompt Flow Build & Refine AI Workflows
    • Tracing & Evaluation Debug and Optimize AI Performance
    • AI Safety & Security Build with Confidence
Module 4: Text Generation on AWS, Azure & GCP Hands-on
  • Amazon Bedrock Text Generation Hands-on
    • Project:Text Generation
      • Leverage Amazon Bedrock to generate high-quality and contextually relevant text.
    • Project:Bedrock model for code generation
      • Using Claude models for code generation
    • Project:Text Summarization
      • Utilize Titan and Claude models to distill complex information into concise summaries.
    • Project:Question Answering (QnA)
      • Build intelligent QnA systems with the capabilities of the Titan model.
    • Entity Extraction
      • Master advanced techniques for extracting critical entities from text.
  • Azure AI Foundry Text Generation Hands-on
    • Azure Authentication & Environment Setup
    • Understanding AIProjectClient
    • Azure AI Foundry Quick Start Guide
    • Project:Chat Completions with AIProjectClient
    • Project:Getting started with Text Embeddings models
    • AI Foundry Prompt Template
    • Phi-4 Model with AIProjectClient
    • Project:Building Advanced Chat Systems with Phi-4
    • Azure OpenAI Studio
      • Azure OpenAI Chat on Private Data with LangChain
      • Azure OpenAI Q&A with Semantic Search Using LlamaIndex
  • Google AI Studio Text Generation
    • Text Generation from Text-Only Inputs
    • Generate Content from Combined Text & Images
    • Real-Time Text Streaming
    • Project:Build Interactive Chat Experiences
    • Handling Long Contexts with Gemini
    • Executing Code with Gemini Basics
    • Producing Structured Responses with the Gemini API
    • Gemini 2.0 Rapid Reasoning & Multi-Turn Dialogues
    • Live Multimodal API Implementation
    • Introduction to Function Execution with the Gemini API
Module 5: Retrieval-Augmented Generation (RAG)
  • Project:Amazon Bedrock Knowledge Bases and RAG
    • Managed RAG
      • Retrieve and generate using managed RAG services.
    • LangChain RAG
      • Implement RAG workflows using LangChain for retrieval and generation.
  • Project:Azure AI Foundry RAG
    • Azure AI Search for RAG-Powered Applications
    • Embeddings Model for RAG-Based Architecture
    • Embedding, Storing & Chatting with Docs in Azure AI Search
    • Bing Grounding: Enhance AI with Web Search Context
  • Project:Vertex AI & Google AI Studio for RAG Solutions
    • RAG architecture using Vertex AI
    • Google Search Grounding: Enrich AI with Real-Time Context
    • Enhance AI with Google Search Suggestions
Module 6: Image Generation and Multimodal Models
  • Bedrock Titan Image Generator
    • Generate high-quality images using Bedrock Titan.
  • Project:Bedrock Amazon Nova
    • Create detailed videos with the power of Amazon Nova Foundation Models.
  • Project:Bedrock Titan Multimodal Embeddings
    • Leverage Titan Multimodal embeddings for advanced multimodal AI tasks.
  • Google AI Studio
    • Imagen 3 in Gemini API
    • Imagen Model Parameters Overview
    • Handling Image & Base64 Inputs with Gemini
    • Video & Text Prompting: Transcription & Visual Descriptions
  • Azure AI Foundry
    • Project:Azure AI Foundry Image Generation Capabilities
    • Embed Images with Azure AI Inference Using Cohere's Model
    • Getting Started with OpenAI DALL·E 3 for Image Generation
Module 7: Customizing Models via Fine-Tuning
  • Model Customization Techniques in Amazon Bedrock
    • Fine-tuning Titan Lite and Llama2 models.
    • Data preparation
    • Customizing hyperparameters
    • Fine-Tuning & Retrieve Custom Model
    • Invoke Custom Model
  • Project:Fine-Tuning with the Gemini API
    • Fine-Tuning Process in Google AI Studio
    • Advanced Tuning Settings with the Gemini API
  • Fine-tune models with Azure AI Foundry
  • LoRA (Low-Rank Adaptation) and QLoRA for parameter-efficient tuning
  • PEFT (Parameter-Efficient Fine-Tuning) techniques
Module 8: AI Agents Hands-on
  • Introduction to AI Agents
  • The Importance of AI Agents
  • Applications and Use Cases of AI Agents
  • Understanding the workflow of AI agents
  • What are AI agents made of?
  • Designing Cutting-Edge AI Agents with AutoGen
  • Project:Agents for Amazon Bedrock (AWS)
    • Components of Bedrock Agents
      • Foundation model
      • Instructions
      • Action groups
      • Knowledge bases for AI Agents
      • Guardrails for Amazon Bedrock
    • Getting started with Amazon Bedrock Agents
  • Project:Building AI Agents using Gemini API (GCP)
    • Building AI Workflows with LangChain
      • Creating an automated essay-writing pipeline
      • Integrating LLMs, prompts, and web search
      • Using LLMChain, ChatPromptTemplate, and StrOutputParser
      • Implementing LangChain Expression Language (LCEL) for task automation
      • Implementing structured data validation with Pydantic
      • Using LLMChain, ChatPromptTemplate, and StrOutputParser
      • Handling real-time information retrieval using Tavily API
    • Iterative AI Agent with LangGraph
    • Deploying and Scaling AI Agents
      • Debugging AI agent issues in LangGraph and LangChain
      • Deploying AI Agents on Vertex AI
  • Project:Azure AI Agents
    • Building AI Agents with Azure
    • Understanding AIProjectClient for managing AI workflows
    • Managing AI Conversations
    • Enhancing AI Agents with Tools
      • Code Interpreter Tool: Performing calculations and analyzing datasets
      • File Search Tool: Searching documents and extracting insights
      • Bing Grounding Tool: Fetching real-time search results
      • Azure AI Search Tool: Connecting AI Agents to a structured search index
    • Using AI for Data Search and Retrieval
      • Setting up Azure AI Search for indexing documents
      • Creating and managing Vector Stores for AI-driven search
    • AI Search Integration for Real-time Information
    • Deploying AI Agents in Production
    • Advanced AI Agent Customization
Module 9: Monitoring & Performance Evaluation
  • Observability in Azure AI
  • Evaluating AI Models Locally
  • Cloud-Based Model Evaluation using AIProjectClient
  • Observability & Tracing in Azure AI
  • Azure Monitor & Application Insights
  • Best Practices for AI Model Monitoring
  • Project:LLM Evaluation & Testing with Evidently AI
    • Evaluate and test your LLM use case
    • Create and evaluate an LLM judge
    • Run regression testing for LLM outputs
  • Project:MLflow for LLM Evaluation
    • Model Evaluation in MLflow
    • Heuristic-Based Evaluation Metrics
    • LLM-as-a-Judge Evaluation Metrics
    • Custom LLM Evaluation Metrics
    • Deploying RAG Systems with MLflow
    • Data Retrieval and Embeddings
    • Evaluating Retrieval Systems
    • Advanced RAG Evaluation with MLflow
  • AWS Bedrock for LLM and RAG Evaluation
  • BLEU, ROUGE, METEOR, and BERTScore for assessing text quality
Module 10: Ethics & Deployment in Generative AI
  • Responsible AI
    • What is responsible AI
    • Challenges of responsible AI
    • Amazon services and tools for responsible AI
    • Building AI responsibly at AWS
    • Core dimensions of responsible AI
    • Project:Implementing safeguards in generative AI
      • Amazon Bedrock Guardrails
    • Azure Responsible AI capabilities
    • Google Cloud’s approach to responsible AI
  • MLOps for LLM’s (LLMOps)
    • What is LLM?
    • MLOps for LLM’s
    • FMOps/LLMOps: Operationalize generative AI
    • LLM System Design
    • High-level view LLM-driven application
    • LLMOps Pipeline
Module 11: Developer-Focused AI Tools
  • Project:Amazon Q for Developer
    • Setting Up Amazon Q Developer
    • Conversing with Amazon Q for Code Assistance
    • Inline Code Suggestions with Amazon Q
    • Code Transformation in the IDE Using Amazon Q
    • Feature Development with Amazon Q
    • Automated Unit Test Generation with Amazon Q
    • Code Review with Amazon Q Developer
    • Auto-Generated Documentation with Amazon Q
    • Supported Languages for Amazon Q in IDE
  • Project:GitHub Copilot
    • Best Practices for GitHub Copilot
    • Automating Tests & Repetitive Code with Copilot
    • Debugging & Fixing Syntax Errors Using Copilot
    • Generating Code Explanations & Comments with Copilot
    • Creating Regular Expressions with Copilot
    • AI-Powered Code Completions with GitHub Copilot
    • Enhancing Code Reviews with GitHub Copilot
    • Streamlining Pull Requests with Copilot Assistance

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$649

$749

Syllabus

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Testimonials

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What you’ll learn

Instructor-led Training

Get trained by top industry experts

Projects and Exercises

Get real-world experience through Projects

Peer Networking and Group Learning

Improve your professional network and learn from peers through our innovative Peer WhatsApp & community groups.

24*7 Technical Support

Speak to Subject Matter Experts anytime and clarify your queries instantly.

Live Hands-on

Hands-on exercises, project work, quizzes, and capstone projects

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