This repository contains a collection of papers related to Machine Learning Operations (MLOps), which is the practice of applying DevOps principles and techniques to machine learning projects. MLOps aims to improve the quality, reliability, scalability and efficiency of machine learning workflows, from data preparation and experimentation to deployment and monitoring.
The papers are organized into the following categories:
• MLOps Overview: Papers that provide a general introduction or survey of MLOps concepts, challenges and solutions.
• MLOps Frameworks: Papers that propose or describe specific frameworks, platforms or tools for implementing MLOps in various scenarios and domains.
• MLOps Techniques: Papers that focus on specific aspects or techniques of MLOps, such as data management, model management, testing, debugging, deployment, monitoring, etc.
• MLOps Applications: Papers that demonstrate the application or evaluation of MLOps in real-world use cases or domains, such as healthcare, finance, e-commerce, etc.
Each paper is accompanied by a brief summary and a link to the full text. The papers are listed in chronological order within each category.
You can use this repository as a reference or a starting point for learning more about MLOps. You can also contribute to this repository by adding new papers, summaries or categories that are relevant to MLOps. To do so, please follow these steps:
• Fork this repository to your own GitHub account.
• Clone your forked repository to your local machine.
• Create a new branch for your changes.
• Add the paper title, summary and link to the appropriate category in the readme file. If the category does not exist yet, create a new one with a suitable name and description.
• Commit and push your changes to your forked repository.
• Create a pull request from your branch to the master branch of this repository.
• Wait for your pull request to be reviewed and merged.
This repository is licensed under the Apache-2.0 license.