Hands on topics you should go through to learn AGENTIC AI

By following this path, you will gradually build up your understanding of Agentic AI, starting with the fundamentals and progressing toward more advanced techniques and applications. Below should be your details of course:

Course Overview

This course is designed for AI enthusiasts and professionals eager to explore Agentic AI and Natural Language Processing (NLP). You will gain hands-on experience in:

Building intelligent systems
Mastering NLP techniques
Developing agentic decision-making frameworks
Implementing Generative AI (GenAI) solutions

By the end, you'll be proficient in creating, deploying, and monitoring advanced NLP and agentic applications.

Learning Objectives

Gain a solid understanding of NLP techniques and applications.
Implement and optimize NLP models for tasks like:
- Text classification
- Sentiment analysis
- Named entity recognition (using SpaCy and Hugging Face).
Explore Generative AI (GenAI) techniques (e.g., Transformers, Language Models).
Develop Agentic Systems with:
- Multi-agent collaboration
- Decision-making frameworks
- Feedback loops
Master Retrieval-Augmented Generation (RAG) for NLP.
Apply frameworks like LangChain and LangFlow to build scalable NLP workflows.
Deploy conversational AI agents with LangGraph, LangFlow, and third-party APIs.
Implement observability techniques using Langfuse and LangWatch.

Course Information

Duration: 5 months (~6 hours per week)
Skill Level: Beginner+
Prerequisites:
- Python proficiency (NumPy, Pandas, Matplotlib)
- Experience with NLP libraries (NLTK, SpaCy, TextBlob)
- Understanding of Deep Learning (TensorFlow, PyTorch, Transformers, GANs, VAEs)

Course Modules and Topics

Module 1: Introduction to Agentic AI

What is Agentic AI?
Agentic AI vs AI Agents and Agentic AI vs Generative AI
Overview of Multi-Agents and their role

Module 2: Phi Data – Agentic AI Framework

Core Concepts: Agents, Models, Tools, Knowledge
Data Storage: Vector Databases, Embeddings
Workflow Design: Execution Strategies
Use Cases: Web Search Agents, Financial Agents, RAG Agents

Module 3: LangChain – NLP Framework

Core Components: Data Ingestion, Document Loaders
Text Splitting: Recursive Splitters, Character/Text Splitters
Embeddings & Vector Storage: OpenAI, Ollama, FAISS, ChromaDB

Module 4: LangChain Expression Language (LCEL)

Using Groq API for Open-Source Models
Building LLMs, Prompt Engineering, Structured Outputs
Deploying LangServe APIs

Module 5: LangServe – AI Deployment

Model Deployment Strategies
API-Driven Model Serving
Integrations with External Tools

Module 6: LangGraph – Workflow Automation

Graph-based AI workflows
State Management: State Schema, State Reducers
Deployment Strategies

Module 7: UX & Human-in-the-Loop with LangGraph

Enhancing User Experience
Dynamic Breakpoints, Streaming, Real-time Feedback

Module 8: Agentic RAG (Retrieval-Augmented Generation)

Adaptive RAG with Cohere
Agentic RAG, C-RAG, Self-RAG with VectorDB

Module 9: Designing Multi-Agent Systems with LangGraph

Agent Collaboration, Communication, and Coordination
Building Scalable Multi-Agent Systems

Module 10: CrewAI – Multi-Agent Workflows

Managing AI Teams
Workflow Automation, Role-based AI Systems

Module 11: LangFlow – AI Workflow Management

Creating AI-driven workflows
LangChain Integration, Prompt Engineering, Pre-built vs. Custom Workflows

Module 12: Integration with Third-Party Tools

Data Sources (SQL, CSV, NoSQL)
API Integration (REST, GraphQL, OpenAI, Hugging Face)
Chatbot Development with LangFlow

Module 13: Langfuse for LLM Observability

Monitoring LLM Performance
Tracking Response Times, Costs, Errors, Prompt Effectiveness

Module 14: LangWatch – Real-Time AI Monitoring

Connecting LangWatch with LLMs
Observability in AI Workflows

Module 15: Langsmith – AI Model Testing

Evaluating AI Model Performance
Workflow Pipelines, Data Preprocessing & Integration

Module 16: AutoGen – Automated AI Model Generation

Designing & Developing Agentic Systems
Agent Interaction, Multi-Agent Collaboration

Module 17: End-to-End Agentic AI Projects

Building and Deploying AI Agents
Decision-Making & Real-World AI Implementation

Cloud Modules: AWS & GCP for GenAI

Module 18: AWS Cloud & Services for GenAI

AWS Account Setup, IAM, Regions & Zones
AWS Compute & Containers: EC2, ECR, App Runner

Module 19: AWS Bedrock – Foundation Models

Bedrock Console & Architecture
Foundation Models, Embeddings, Chat Playgrounds
Inference Parameters & Pricing

Module 20: AWS SageMaker – ML & AI Workflows

SageMaker Studio, Model Training & Deployment
Pre-trained Models, SageMaker Endpoints

Module 21: AWS Lambda – Serverless AI

Deploying AI with AWS Lambda
Integration with AWS Services

Module 22: AWS API Gateway – API Development

Creating RESTful & WebSocket APIs
Lambda & API Gateway Integration

Module 23: Text Summarization with AWS Services

AWS Lambda & API Gateway with Bedrock
RESTful API for AI Summarization

Module 24: Fine-Tuning AI Models with AWS

Fine-Tuning Foundation Models
Custom Data Training with SageMaker

Module 25: RAG & AI Chatbot with AWS

Building a Retrieval-Augmented Generation (RAG) System
Llama3, LangChain, Streamlit Chatbot

GCP Modules: AI with Google Cloud & Vertex AI

Module 26: GCP Basics & Vertex AI

Google AI Studio, Vertex AI Model Garden
Google Cloud Setup for AI

Module 27: Google Gemini AI Models

Playing with Gemini, Gemini 1.5 Pro, Gemini 1.0 Pro
Embeddings, Information Retrieval, Multimodal Applications

Module 28: RAG & AI Chatbot with GCP

Building a RAG System on GCP
Chatbot with Gemini Pro, LangChain & Streamlit

Conclusion

This course offers a comprehensive learning path for AI professionals looking to master Agentic AI, Generative AI, NLP, and Cloud AI deployment on AWS & GCP. By the end, you’ll have hands-on experience in building, deploying, and optimizing AI workflows across various platforms.