AI: Rust for Large Language Model Operations (LLMOps)

by Pragmatic AI Labs

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.

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