Github Raminmohammadi Mlops Machine Learning In Production Mlops

Welcome to the MLOps Repository! This repository is dedicated to sharing reading contents, labs and exercises for the MLOps (Machine Learning Operations) course at Northeastern University. The primary goal of this repository is to provide a centralized platform for students, instructors, and anyone interested in MLOps to access and collaborate on course-related materials. You can learn more on Machine learning topics by watching my videos on Youtube or visit my Website. MLOps is an emerging discipline that focuses on the collaboration and communication of both data scientists and IT professionals while automating and streamlining the machine learning lifecycle. It bridges the gap between machine learning development and production deployment, ensuring that machine learning models are scalable, reproducible, and maintainable.

This repository serves as a resource hub for students and instructors of Northeastern University's MLOps course. The MLOps course at Northeastern University is designed to provide students with a comprehensive understanding of the MLOps field. Throughout the course, students will learn how to: This repository hosts the labs, code samples, and documentation related to these topics. This repository offers a series of hands-on labs designed to enhance your understanding of MLOps and LLMOps concepts. Each lab focuses on a specific aspect of the machine learning lifecycle, providing practical experience with tools and methodologies essential for deploying and managing both traditional ML models and modern LLMs in production environments.

I’m an AI/ML engineer and technical leader with over 8 years of experience building scalable machine learning systems across domains like aerospace and defense, healthcare, cybersecurity, and enterprise automation. My work spans end-to-end ML pipelines, federated learning, NLP, MLOps, and Generative AI—from architecting infrastructure to deploying models in production environments with tight privacy and compliance constraints. I’ve led cross-functional ML teams, developed LLM-powered automation agents, built streaming data pipelines, and helped drive AI-first product strategy through fast, high-impact iterations. My focus is always on bridging the gap between research and real-world implementation. I also serve as an Adjunct Professor at Northeastern University, where I teach graduate and Ph.D.-level courses in Machine Learning, MLOps, NLP, and Generative AI. I lead a team of teaching assistants, mentor project-based learning at scale, and help students land roles in applied AI.

Explore my repositories to find work in MLOps, Generative AI, RAG pipelines, and NLP, and feel free to reach out if you’d like to collaborate or geek out about building real, production-ready AI systems. Listen to the episode using the link below: Lead Principal MLE | Change Leader | Professor | Mlops I am excited to announce the launch of my MLOPS repository tailored for both students and professionals keen on diving into the intricacies of MLOPS. This repository serves as a comprehensive resource hub where enthusiasts can access labs, tutorials, and guides aimed at enhancing their understanding and mastery of MLOPS practices. What sets this repository apart is its dynamic nature – it's constantly evolving with new updates and materials being added regularly to keep pace with the ever-changing landscape of MLOPS.

Whether you're just starting your journey or seeking to refine your skills, this repository offers a structured pathway for learning and growth in the exciting field of MLOPS. Join me on this journey of exploration and innovation as we navigate through the world of machine learning operations together. I highly value collaboration and welcome contributions aimed at enhancing the quality and breadth of materials available in the repository. Your input and expertise are invaluable in shaping a more comprehensive and impactful resource for the MLOPS community. Thank you for your willingness to collaborate and contribute to our shared learning journey. Graduate of the Northeastern University program in Analytics and Applied Machine Intelligence.

Physician with a passion for bridging AI and health care. Business Intelligence | Azure AI | NLP | MS in Data Analytics Engineering | Ex - Analyst at Harvard, GMO MS Data Science & Analytics | Data Analyst | Data Visualization | Python | SQL Welcome to the MLOps Repository! This repository is dedicated to sharing reading contents, labs and exercises for the MLOps (Machine Learning Operations) course at Northeastern University. The primary goal of this repository is to provide a centralized platform for students, instructors, and anyone interested in MLOps to access and collaborate on course-related materials.

You can learn more on Machine learning topics by watching my videos on Youtube or visit my Website. MLOps is an emerging discipline that focuses on the collaboration and communication of both data scientists and IT professionals while automating and streamlining the machine learning lifecycle. It bridges the gap between machine learning development and production deployment, ensuring that machine learning models are scalable, reproducible, and maintainable. This repository serves as a resource hub for students and instructors of Northeastern University's MLOps course. The MLOps course at Northeastern University is designed to provide students with a comprehensive understanding of the MLOps field. Throughout the course, students will learn how to:

This repository hosts the labs, code samples, and documentation related to these topics. This repository offers a series of hands-on labs designed to enhance your understanding of MLOps concepts. Each lab focuses on a specific aspect of the machine learning lifecycle, providing practical experience with tools and methodologies essential for deploying and managing machine learning models in production environments. Instantly share code, notes, and snippets. In the age of data-driven decision-making, machine learning (ML) has become a cornerstone for businesses across industries. However, deploying ML models and maintaining them in production requires more than just coding skills; it demands a solid understanding of MLOps (Machine Learning Operations).

To help you navigate this crucial field, we've curated a list of 10 GitHub repositories that offer valuable resources, tools, and frameworks to help you master MLOps. In this article, we will explore, 10 GitHub Repositories to Master MLOps. These 10 GitHub repositories offer a diverse range of tools to help you build, scale, and monitor machine-learning models in production environments. Description: This repository hosts a collection of Jupyter notebooks that showcase the various capabilities of Azure Machine Learning. You'll find practical examples of model training, deployment, and MLOps workflows, making it a great starting point for those interested in Azure's ecosystem. Link: https://github.com/Azure/MachineLearningNotebooks

Description: This repository provides a practical implementation of MLOps using Python and Azure. It covers the entire ML lifecycle—from data preparation to deployment and monitoring—making it an excellent resource for hands-on learning. Begin your MLOps journey with these comprehensive free resources available on GitHub. It is becoming more important to master MLOps (Machine Learning Operations) for those who want to effectively deploy, monitor, and maintain their ML models in production. MLOps is a set of practices that aims to merge ML system development (Dev) and ML system operation (Ops). Luckily, the open-source community has created numerous resources to assist beginners in mastering these concepts and tools.

Here are ten GitHub repositories that are essential for anyone looking to master MLOps: It is a 9-week study plan designed to help you master various concepts and tools related to Model Monitoring, Configurations, Data Versioning, Model Packaging, Docker, GitHub Actions, and AWS Cloud. You will learn how to build an end-to-end MLOps project, and each week will focus on a specific topic to help you achieve this goal. The repository provides MLOps end-to-end examples & solutions. A collection of examples showing different end to end scenarios operationalizing ML workflows with Azure Machine Learning, integrated with GitHub and other Azure services such as Data Factory and DevOps.

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