Rust for Large Language Model Operations (LLMOps)

Overview

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Do you aspire to be a Rust developer at the forefront of the AI revolution? This groundbreaking course is designed specifically to train you in Large Language Model Operations (LLMOps) using Rust. This course doesn't just scratch the surface; it takes a deep dive into how you can integrate Rust with sophisticated LLM frameworks like HuggingFace Transformers. We'll also explore how to effectively deploy these large models on cloud infrastructures such as AWS, all while incorporating DevOps methodologies tailored for LLMOps.

Syllabus

DevOps Concepts for MLOps

Do you aspire to be a Rust developer at the forefront of the AI revolution? This groundbreaking 4-week course is designed specifically to train you in Large Language Model Operations (LLMOps) using Rust. This course doesn't just scratch the surface; it takes a deep dive into how you can integrate Rust with sophisticated LLM frameworks like HuggingFace Transformers. We'll also explore how to effectively deploy these large models on cloud infrastructures such as AWS, all while incorporating DevOps methodologies tailored for LLMOps.

Rust Hugging Face Candle

This week, you will delve into the powerful combination of Rust with Candle, a minimalist ML framework, and explore how they can be used with Hugging Face's popular transformer models. You will apply these concepts by working on a series of hands-on labs that guide you through building, training, and deploying machine learning models using Rust, Candle, and Hugging Face. The assessment will challenge you to create a real-world application using these tools, demonstrating your ability to apply the techniques learned in complex scenarios.

Key LLMOps Technologies

This week, you will learn how to implement state-of-the-art natural language processing models in Rust using key LLMOps technologies like Rust Bert, tch-rs, and ONNX. You will apply these skills by converting a BERT model to ONNX and deploying it in a Rust application, demonstrating proficiency in operationalizing NLP pipelines.