Rust for Large Language Model Operations (LLMOps)

Course Description

This advanced course, "Rust for Large Language Model Operations (LLMOps)," is a cutting-edge program designed to equip students with the skills needed to excel in the rapidly evolving field of AI development. By combining the power of Rust programming language with Large Language Model Operations, this course offers a unique opportunity to master the creation of scalable, high-performance LLM solutions.

What students will learn from the course:

  • Advanced Rust programming techniques for AI/ML applications
  • Integration of Rust with popular LLM frameworks like HuggingFace Transformers, Candle, and ONNX
  • Optimization of LLM training and inference using Rust's parallelism and GPU acceleration
  • Building and deploying BERT models in Rust applications via ONNX runtime
  • Hosting and scaling LLM solutions on AWS cloud infrastructure
  • Implementation of LLMOps DevOps practices, including CI/CD, monitoring, and security
  • Best practices for LLMOps reliability, scalability, and cost optimization
  • Real-world project development demonstrating production-ready LLMOps expertise

Pre-requisites or skills necessary to complete the course:

While the course is listed as introductory level, students should have:

  • Basic programming knowledge, preferably in Rust or a similar language
  • Familiarity with AI/ML concepts
  • Understanding of cloud computing basics
  • Comfort with command-line interfaces and development environments

What the course will cover:

  • Rust programming fundamentals and advanced techniques
  • LLM frameworks and their integration with Rust
  • GPU acceleration and parallel computing in Rust
  • Cloud deployment and scaling of LLM solutions
  • DevOps and LLMOps best practices
  • Memory safety, multithreading, and lock-free concurrency in Rust
  • Hands-on projects: chatbots, text summarizers, machine translation
  • Real-world LLMOps application development

Who this course is for:

This course is ideal for:

  • Software developers looking to transition into AI/ML
  • AI/ML practitioners seeking to leverage Rust's performance benefits
  • Data scientists interested in building scalable LLM solutions
  • DevOps engineers wanting to specialize in LLMOps
  • Students and professionals aiming to stay at the forefront of AI technology

How learners can use these skills in the real world:

Graduates of this course will be well-equipped to:

  • Develop high-performance, scalable AI applications
  • Optimize existing LLM solutions for better efficiency and cost-effectiveness
  • Implement robust LLMOps practices in production environments
  • Create innovative AI products leveraging cutting-edge LLM technologies
  • Contribute to open-source LLM projects and frameworks
  • Pursue careers in AI research, development, and engineering roles

Syllabus:

Module 1: DevOps Concepts for MLOps (6 hours)

  • Introduction to DevOps and MLOps
  • Continuous Integration and Delivery for Microservices
  • Infrastructure as Code and AWS Security
  • GitHub ecosystem and automation tools

Module 2: Rust Hugging Face Candle (4 hours)

  • Introduction to Candle framework
  • GPU inference with Rust Candle
  • Exploring state-of-the-art LLMs (StarCoder, Falcon, Whisper)
  • Serverless and CLI inference techniques

Module 3: Key LLMOps Technologies (3 hours)

  • Rust Bert: Installation, setup, and basic usage
  • Rust PyTorch integration
  • ONNX conversions and implementations

Module 4: Key Generative AI Technologies (3 hours)

  • Google Bard and Colab AI exploration
  • AWS Bedrock and responsible AI practices
  • AWS CodeWhisperer for AI-assisted coding

The course includes a mix of video lectures, readings, hands-on labs, and quizzes to ensure comprehensive learning and practical application of the concepts covered.