NLP & Transformers

Natural Language Processing (NLP) is the branch of AI focused on enabling computers to understand, interpret, and generate human language. The transformer architecture, introduced in the "Attention Is All You Need" paper, revolutionized the field.

Key concepts include tokenization (BPE, WordPiece, SentencePiece), attention mechanisms (self-attention, multi-head attention, cross-attention), transformer architectures (encoder-only like BERT, decoder-only like GPT, encoder-decoder like T5), and classic NLP tasks like sentiment analysis, named entity recognition, text classification, and machine translation.

Understanding transformers is essential for modern ML engineering. From BERT's bidirectional encoding to GPT's autoregressive generation, these architectures underpin virtually all state-of-the-art language models today.

Overview

ML Concepts

What is the attention mechanism in transformers?

How do you manage context windows in LLM applications?

Deep-Dive Concepts (from Projects)

Transfer Learning with BERT

Model Quantization for Production

Evaluation Metrics Beyond Accuracy

FastAPI for ML Model Serving

Model Context Protocol (MCP) Explained

AI Voice Agent Architecture

Tool Orchestration in Multi-Tool AI Systems

Webhook Design for AI Agents

Client-Side Tool Integration for Voice Agents

Production Voice Agent Deployment

QLoRA: Quantized Low-Rank Adaptation

vLLM Inference Optimization

TGI: Text Generation Inference

FSDP: Fully Sharded Data Parallel

DeepSpeed ZeRO: Zero Redundancy Optimizer

Multi-Node Training Networking

LoRA vs Full Fine-Tuning

Feature Store Architecture

Spot Training Economics

Multi-Model Endpoints

Model Monitor Metrics

SageMaker Pipelines vs Step Functions

Serverless Inference Trade-offs

GPU Instance Selection

Clarify Bias Metrics

SageMaker vs Self-Hosted MLOps

Distributed Training Strategies

MLOps Platform Comparison: SageMaker vs Vertex AI vs Kubeflow vs Databricks

NLU Pipeline: From Text to Intent

Containment Rate: The Key CCAI Metric