This repository provides course materials for Northeastern University's MLOps (Machine Learning Operations) program, offering a centralized resource for students, instructors, and enthusiasts. It aims to bridge the gap between ML development and production by covering essential concepts and practical labs for building, deploying, monitoring, and maintaining ML systems, including a focus on LLMOps.

How It Works

The project serves as a practical learning hub, delivering hands-on labs, code samples, and reading content for MLOps and LLMOps. It covers the end-to-end machine learning lifecycle, emphasizing practical implementation of containerization, orchestration, CI/CD/CM/CT pipelines, model monitoring, retraining strategies, and responsible AI practices for both traditional ML models and large language models. The labs are designed to be followed sequentially, building upon foundational concepts.

Quick Start & Requirements

• Primary install/run command: Clone the repository (git clone). Navigate to specific lab directories for instructions and code.
• Prerequisites: Specific hardware, software (e.g., GPU, CUDA, Python versions), or datasets are not detailed in this overview README; users must consult individual lab documentation. Assumes familiarity with core ML/LLM concepts.
• Setup time/footprint: Not specified.
• Links: Mentions YouTube videos and a personal website for further learning, but no URLs are provided.

Highlighted Details

• Comprehensive coverage of MLOps principles including CI/CD/CM/CT for ML.
• Dedicated labs for LLMOps, focusing on evaluation, alignment, monitoring, deployment, and lifecycle management of Large Language Models.
• Practical exercises in containerization (e.g., Docker), orchestration, model monitoring, and handling data drift.
• Structured learning path with labs designed to build upon each other.

Maintenance & Community

Contributions are welcomed via pull requests. The repository does not list specific maintainers, community channels (like Discord/Slack), or a public roadmap.

Licensing & Compatibility

Licensed under the MIT License. However, a significant NEU-Specific Restriction prohibits the use, reproduction, or distribution of this content for the same or similar courses within Northeastern University without prior written permission. This may impact commercial or academic adoption outside of the intended course context.

Limitations & Caveats

The primary caveat is the NEU-Specific Restriction, limiting its use for similar academic purposes within Northeastern University. Detailed setup instructions, specific dependency versions, and hardware requirements for individual labs are not consolidated in this main README and must be sought within each lab's specific documentation. The repository's focus is educational, not a production-ready framework.