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raminmohammadi/MLOps

Course labs, reading materials and exercises for an MLOps and LLMOps course at Northeastern University, covering ML deployment and monitoring.

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What is it?

This repository collects the labs, code samples and reading materials for a Northeastern University MLOps course (DADS 7305). It is organized as a resource hub for students and instructors, with hands-on labs that follow the machine learning lifecycle from building pipelines to deploying and maintaining models in production.

The covered topics include containerization and orchestration, CI/CD and continuous training for ML, monitoring and handling data drift, and retraining. The course also includes LLMOps labs on evaluation, alignment, monitoring and deployment of large language models. Labs are meant to be followed in sequence, with instructions and code examples in each folder.

The reading materials are drawn from Coursera under Creative Commons terms. Code is MIT-licensed, but the README restricts use of this content for similar courses within Northeastern University without written permission.

Key features

  • 01Hands-on labs covering the ML lifecycle from pipelines to production
  • 02Containerization, orchestration and CI/CD pipelines for ML workloads
  • 03Model monitoring, retraining and data drift handling exercises
  • 04LLMOps labs on evaluation, alignment and LLM deployment

Use cases

  • Following a university MLOps course with sequenced labs and code
  • Learning how to move machine learning models into production
  • Exploring LLMOps practices for evaluating and deploying large language models

Quick start

  1. 1

    Clone this repository to your local machine

  2. 2

    Navigate to the specific lab you are interested in

  3. 3

    Read the lab instructions and follow the provided code samples

Things to know

  • This is course material (labs and readings), not a packaged software project, so there is no single install or run command.
  • The README says MIT licensed but also bars use for the same or similar Northeastern University courses without written permission, while the metadata lists CC0-1.0.

In collections

Learning & education toolsTesting frameworks & test automation tools

Topics

#mlops#machine-learning#llmops#ci-cd#model-deployment#monitoring#education

FAQ

What is MLOps?
Course labs, reading materials and exercises for an MLOps and LLMOps course at Northeastern University, covering ML deployment and monitoring. This repository collects the labs, code samples and reading materials for a Northeastern University MLOps course (DADS 7305). It is organized as a resource hub for students and instructors, with hands-on labs that follow the machine learning lifecycle from building pipelines to deploying and maintaining models in production.
Is MLOps free to use? What license does it have?
MLOps is open source under the CC0-1.0 license. Check the license terms before using it in commercial products.
Is MLOps still maintained?
Actively maintained. The last commit to MLOps was on Oct 11, 2026.
What can I use MLOps for?
Following a university MLOps course with sequenced labs and code Learning how to move machine learning models into production Exploring LLMOps practices for evaluating and deploying large language models
How do I install MLOps?
Clone this repository to your local machine Navigate to the specific lab you are interested in Read the lab instructions and follow the provided code samples
Where can I get MLOps?
The source code and installation instructions are on GitHub: https://github.com/raminmohammadi/MLOps

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