Blog | Pragmatic AI Labs

Production LLM Systems with AWS: A 10-Week Technical Deep Dive

· 7min · Pragmatic AI Labs
Table of Contents

Large Language Model Operations (LLMOps)

Welcome to the Large Language Model Operations course. This intensive program will teach you how to build, deploy, and maintain production-ready LLM applications using industry best practices. By combining hands-on projects with comprehensive theoretical understanding, you'll develop the skills needed to succeed in the rapidly evolving field of AI operations.

Course Description

This course provides comprehensive training in operationalizing Large Language Models, enabling you to develop production-ready applications using software development best practices. Through a series of weekly mini-projects culminating in a final project, you will gain hands-on experience in building, deploying, and maintaining LLM-powered applications.

Prerequisites

Students should have basic programming skills in Python or Rust. If you need to strengthen your foundation, complete Python, Bash and SQL Essentials courses before beginning this course.

Course Resources

The following resources form the core curriculum of this course. You will need to access these throughout the term:

Week 1: Natural Language AI with Bedrock https://ds500.paiml.com/code

Week 2: AI-Orchestration https://ds500.paiml.com/code

Week 3: AWS Enterprise AI Solutions https://ds500.paiml.com/code

Week 4: Advanced AI Analytics https://ds500.paiml.com/code

Week 5: Generative AI with AWS https://ds500.paiml.com/code

Week 6: AWS Generative AI Services https://ds500.paiml.com/code

Week 7: CLI Automation with Amazon Q https://ds500.paiml.com/code

Week 8: Open Source LLMs on AWS: From Compilation to Deployment https://ds500.paiml.com/code

Week 9: Building AI Applications with Amazon Bedrock https://ds500.paiml.com/code

Week 10: Responsible AI and Security on AWS https://ds500.paiml.com/code

Weekly Schedule and Projects

Week 1: Foundations of Natural Language AI

In our first week, we'll establish the groundwork for working with large language models using Amazon Bedrock. You'll learn the fundamentals of natural language processing and begin working with AI models.

Mini-Project: Build a Conversational AI Assistant

  • Implement basic conversation flow using Amazon Bedrock
  • Add context management and memory
  • Create comprehensive error handling
  • Document API patterns and usage

Deliverables:

  • Working conversational AI application
  • Technical documentation
  • Test suite with 80% coverage
  • 5-minute demonstration video

Week 2: AI Orchestration Fundamentals

Building on our foundation, we'll explore how to orchestrate AI workflows effectively, ensuring reliable and scalable operations.

Mini-Project: Create an AI Pipeline

  • Design and implement an orchestrated workflow
  • Add monitoring and logging
  • Implement retry mechanisms
  • Create comprehensive documentation

Deliverables:

  • Functional pipeline implementation
  • Architecture documentation
  • Monitoring dashboard
  • Technical writeup

Week 3: Enterprise AI Solutions

This week focuses on building enterprise-grade AI solutions that meet business requirements for security, scalability, and reliability.

Mini-Project: Enterprise Chat Application

  • Build a secure chat interface
  • Implement user authentication
  • Add audit logging
  • Create deployment documentation

Deliverables:

  • Working enterprise application
  • Security documentation
  • Deployment guide
  • Performance analysis

Week 4: Advanced Analytics Integration

Learn to integrate analytics capabilities into your AI applications, enabling data-driven insights and monitoring.

Mini-Project: Analytics Dashboard

  • Create data processing pipeline
  • Build visualization components
  • Implement real-time monitoring
  • Document system architecture

Deliverables:

  • Working dashboard
  • Data flow documentation
  • Performance metrics
  • User guide

Week 5: AWS Generative AI Implementation

Explore advanced generative AI capabilities using AWS services, focusing on practical applications and best practices.

Mini-Project: Text Generation Service

  • Implement text generation API
  • Add content filtering
  • Create usage monitoring
  • Document API endpoints

Deliverables:

  • Working API service
  • API documentation
  • Security measures
  • Usage metrics

Week 6: Production AI Services

Learn to build and maintain production-ready AI services that can scale with demand and maintain high availability.

Mini-Project: Multi-Modal AI Service

  • Build image and text processing pipeline
  • Implement service mesh
  • Add performance monitoring
  • Create service documentation

Deliverables:

  • Working service
  • Architecture documentation
  • Performance analysis
  • Deployment guide

Week 7: CLI and Automation

Focus on building efficient command-line tools and automation workflows using Amazon Q.

Mini-Project: AI-Powered CLI Tool

  • Create command-line interface
  • Implement automation scripts
  • Add error handling
  • Write user documentation

Deliverables:

  • Working CLI tool
  • Test coverage report
  • User manual
  • Demo video

Week 8: Open Source LLM Integration

Learn to work with open source language models, from selection to deployment on AWS infrastructure.

Mini-Project: Local LLM Deployment

  • Deploy open source LLM
  • Create API wrapper
  • Implement caching
  • Document deployment process

Deliverables:

  • Working local LLM
  • API documentation
  • Performance metrics
  • Deployment guide

Week 9: Application Development with Bedrock

Develop full-stack applications using Amazon Bedrock, incorporating best practices for production deployments.

Mini-Project: Full-Stack AI Application

  • Create frontend interface
  • Build backend services
  • Implement authentication
  • Add monitoring

Deliverables:

  • Complete application
  • Architecture document
  • User guide
  • Security documentation

Week 10: Responsible AI and Security

Conclude the course by focusing on responsible AI practices and securing AI applications.

Mini-Project: Security Implementation

  • Add security measures
  • Implement privacy controls
  • Create audit system
  • Document security architecture

Deliverables:

  • Security implementation
  • Audit documentation
  • Compliance report
  • Final presentation

Final Project

The course culminates in a comprehensive final project that demonstrates mastery of the concepts covered throughout the term. Your final project should incorporate elements from each week's learning while solving a real-world problem.

Project Requirements

Technical Implementation (40%):

  • Architecture design and implementation
  • Code quality and testing
  • Performance and scalability
  • Error handling and resilience

Documentation (30%):

  • Technical documentation
  • API documentation
  • Deployment guide
  • User manual

Security and Responsibility (30%):

  • Security measures
  • Privacy controls
  • Responsible AI practices
  • Compliance documentation

Grading Structure

Your final grade will be calculated as follows:

  • Weekly Mini-Projects: 60% (6% each)
  • Final Project: 40%

Submission Guidelines

All project submissions must include:

  • GitHub repository with complete code
  • Documentation in Markdown format
  • Working demonstration
  • 5-minute presentation video
  • Test coverage report

Required Tools

To participate in this course, you will need:

  • AWS Account (provided through AWS Academy)
  • GitHub Account
  • Development Environment (VS Code recommended)
  • Video Recording Software

Support Resources

We provide several channels for support:

  • Course discussion forums
  • Weekly office hours
  • GitHub issues
  • Email support

Academic Integrity

All work must be original and individual unless explicitly specified as group work. Use of AI assistants and code generation tools must be documented and attributed appropriately.

Want expert ML/AI training? Visit paiml.com

For hands-on courses: DS500 Platform

Based on this article's content, here are some courses that might interest you:

  1. AWS Advanced AI Engineering (1 week) Production LLM architecture patterns using Rust, AWS, and Bedrock.

  2. Natural Language AI with Bedrock (1 week) Get started with Natural Language Processing using Amazon Bedrock in this introductory course focused on building basic NLP applications. Learn the fundamentals of text processing pipelines and how to leverage Bedrock's core features while following AWS best practices.

  3. Enterprise AI Operations with AWS (2 weeks) Master enterprise AI operations with AWS services

  4. Generative AI with AWS (4 weeks) This GenAI course will guide you through everything you need to know to use generative AI on AWS - an introduction on using Generative AI with AWS

  5. Building AI Applications with Amazon Bedrock (4 weeks) Learn Building AI Applications with Amazon Bedrock

Learn more at Pragmatic AI Labs