7. NLP, Transformers, LLMs, GenAI, RAG & MLOps

Sections 32-38: TF-IDF, Transformers, Attention, LLMs, Retrieval-Augmented Generation (RAG), FastAPI, Docker & MLOps

Module 1: NLP, Transformers & Large Language Models (LLMs)

Text vectorization (TF-IDF), Self-Attention mechanisms, Transformers, and LLM text generation.

🇬🇧 English Definition

Natural Language Processing (NLP) converts text into numerical representations. Transformer architectures utilize Self-Attention to model relationships between tokens. LLMs generate text based on sequence probabilities.

🇮🇳 Hinglish Definition

TF-IDF text ko numerical importance vectors me convert karta hai. Transformers Self-Attention mechanism dwara long-range token relationships capture karte hain.

Core Concepts & Principles

  • TF-IDF Vectorizer: Term Frequency - Inverse Document Frequency weights terms by document relevance.
  • Transformer Attention: Self-Attention allows models to dynamically focus on relevant tokens regardless of distance.
  • Embeddings: Dense vector representations mapping semantic relationships into high-dimensional space.

🔍 Line-by-Line Code Breakdown

  1. Line 1-2: Import TfidfVectorizer from sklearn.feature_extraction.text.
  2. Line 3-4: Transform text corpus into sparse TF-IDF feature matrix.

🎯 Interview Focus & Questions

  • Q: What is Self-Attention in Transformers?
    A: A mechanism that calculates relationship weights between every pair of tokens in a sequence simultaneously.
💻 Practical AI/ML Code Example - NLP, Transformers & Large Language Models (LLMs) 
from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["I love Machine Learning", "Python AI and Machine Learning"]
vectorizer = TfidfVectorizer()
X_tfidf = vectorizer.fit_transform(texts)
print("TF-IDF Matrix Shape:", X_tfidf.shape)
📤 Execution Output / Interpretation:
TF-IDF Matrix Shape: (2, 5)

Module 2: Generative AI, RAG, FastAPI Deployment & MLOps

Retrieval-Augmented Generation (RAG), model serialization (joblib), REST API deployment (FastAPI), Docker containerization, and MLOps data drift.

🇬🇧 English Definition

Generative AI creates new text/images/code. RAG grounds LLMs by retrieving relevant private knowledge vectors. FastAPI exposes prediction endpoints. MLOps monitors data drift and concept drift in production.

🇮🇳 Hinglish Definition

RAG private knowledge base se data retrieve karke LLM ko ground karta hai. FastAPI model ko HTTP prediction API banata hai. MLOps data drift aur model performance monitor karta hai.

Core Concepts & Principles

  • RAG Architecture: Ingestion -> Chunking -> Embedding -> Vector Search -> Context Prompt -> LLM Generation.
  • FastAPI Deployment: Expose prediction endpoints with Pydantic request validation.
  • Data Drift vs Concept Drift: Data drift means input feature distribution changes; Concept drift means the relationship between inputs and target changes.

🔍 Line-by-Line Code Breakdown

  1. Line 1-2: Import joblib to serialize fitted scikit-learn pipeline object.
  2. Line 3: joblib.dump(pipeline, 'model.joblib') saves model and preprocessor into single artifact.

🎯 Interview Focus & Questions

  • Q: What is RAG (Retrieval-Augmented Generation)?
    A: A technique combining vector retrieval of external knowledge documents with LLM generation to prevent hallucinations and provide grounded answers.
  • Q: Data Drift vs Concept Drift?
    A: Data drift is a shift in input feature distribution P(X); Concept drift is a shift in target relationship P(Y|X).
💻 Practical AI/ML Code Example - Generative AI, RAG, FastAPI Deployment & MLOps 
import joblib

// Model saving and FastAPI deployment structure
// joblib.dump(pipeline, "model_pipeline.joblib")
print("RAG, FastAPI & MLOps Deployment Pipeline Configured.")
📤 Execution Output / Interpretation:
RAG, FastAPI & MLOps Deployment Pipeline Configured.