Blog | Pragmatic AI Labs

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

2024-01-27

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:

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

Deliverables:

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

Deliverables:

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

Deliverables:

Week 4: Advanced Analytics Integration

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

Mini-Project: Analytics Dashboard

Deliverables:

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

Deliverables:

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

Deliverables:

Week 7: CLI and Automation

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

Mini-Project: AI-Powered CLI Tool

Deliverables:

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

Deliverables:

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

Deliverables:

Week 10: Responsible AI and Security

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

Mini-Project: Security Implementation

Deliverables:

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%):

Documentation (30%):

Security and Responsibility (30%):

Grading Structure

Your final grade will be calculated as follows:

Submission Guidelines

All project submissions must include:

Required Tools

To participate in this course, you will need:

Support Resources

We provide several channels for 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.