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600: Frameworks & Tools

Topic Overview

Building complex AI applications from scratch is a significant undertaking. Fortunately, the AI community has developed powerful open-source frameworks and platforms that provide building blocks and abstractions to dramatically accelerate the development process.

This section covers the essential tools that every AI Engineer should be familiar with.

The Modern AI Engineer's Stack

A typical AI application is built not just on a model, but on an ecosystem of tools that handle everything from data processing to model interaction and deployment.

graph TD
    subgraph "User-Facing Application"
        A[Your Application Logic]
    end

    subgraph "Orchestration Layer"
        B[LangChain<br/>Chains, Agents, RAG Pipelines]
    end

    subgraph "Model & Data Hub"
        C[Hugging Face<br/>Models, Datasets, Tokenizers]
    end

    subgraph "Core Computation"
        D[PyTorch / TensorFlow<br/>Neural Network Operations]
    end

    A --> B
    B --> C
    C --> D

    style A fill:#e3f2fd,stroke:#1976d2
    style B fill:#e8f5e8,stroke:#388e3c
    style C fill:#fff3e0,stroke:#f57c00
    style D fill:#fce4ec,stroke:#c2185b

Framework Categories

Orchestration Frameworks

These tools help you chain together multiple AI operations and manage complex workflows:

  • LangChain: The most popular framework for building LLM applications
  • LlamaIndex: Specialized for data ingestion and retrieval-augmented generation
  • Haystack: Enterprise-focused NLP framework with strong search capabilities

Model & Data Platforms

Centralized hubs for accessing pre-trained models and datasets:

  • Hugging Face: The GitHub of machine learning models
  • Weights & Biases: Experiment tracking and model versioning
  • OpenAI Platform: API access to GPT models and tools

Deployment & Infrastructure

Tools for taking your AI applications to production:

  • Streamlit: Rapid prototyping of ML web applications
  • Gradio: Quick interfaces for machine learning models
  • Docker: Containerization for consistent deployment environments

Why Frameworks Matter

Modern AI engineering isn't about building everything from scratch. It's about understanding how to effectively combine existing tools to solve complex problems. These frameworks provide:

  • Standardized interfaces for common operations
  • Battle-tested implementations of complex algorithms
  • Community ecosystems with extensive documentation and examples
  • Rapid iteration cycles for experimentation and prototyping

The remainder of this section will dive deep into the most essential frameworks, starting with LangChain as the foundation for most modern LLM applications.

Learning Path

We recommend learning these frameworks in order: 1. LangChain - Master the fundamentals of LLM application architecture 2. Hugging Face - Understand model access and fine-tuning 3. Deployment Tools - Learn to ship your applications to users