16 个仓库
Awesome GitHub RepositoriesMLOps Articles
Technical guides, case studies, and research on operationalizing machine learning.
Explore 16 awesome GitHub repositories matching part of an awesome list · MLOps Articles. Refine with filters or upvote what's useful.
Awesome MLOps Articles GitHub Repositories
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- graviraja/mlops-basics
graviraja/MLOps-Basics
8,585在 GitHub 上查看MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz
Foundational concepts for getting started with MLOps.
Jupyter Notebook - alirezadir/production-level-deep-learning
alirezadir/Production-Level-Deep-Learning
4,647在 GitHub 上查看本项目是一套 MLOps 架构指南和框架，旨在设计并将深度学习系统部署到生产环境。它为模型推理部署、机器学习流水线编排以及生产级机器学习架构的构建提供了结构化的方法。 该项目的特色在于专注于分布式深度学习和边缘 AI 优化。它涵盖了在多个 GPU 上并行化模型训练以处理大规模数据集的方法，并应用了量化和蒸馏等技术来减小嵌入式硬件上的模型体积。 其功能范围还扩展到了监控和可观测性，包括跟踪模型性能、数据漂移和实验指标。此外，它还解决了数据工作流编排、通过对象存储进行数据集版本控制，以及使用自适应批处理和容器化编排来管理高并发推理请求的问题。
Best practices for deploying deep learning models at scale.
aiartificial-intelligencedeep-learning - se-ml/awesome-seml
SE-ML/awesome-seml
1,358在 GitHub 上查看A curated list of articles that cover the software engineering best practices for building machine learning applications.
Curated list of software engineering practices for machine learning.
awesomeawesome-listdeep-learning - dslp/dslp-repo-template
dslp/dslp-repo-template
202在 GitHub 上查看Template repository for data science lifecycle project
Template for structuring data science and machine learning projects.
Python - visenger/ml-project-template
visenger/ml-project-template
Standardized directory structure for organizing machine learning projects.
- bartgras/4ab9c716167b5d9aee6a222f7301ac60
bartgras/4ab9c716167b5d9aee6a222f7301ac60
Reference for MLOps implementation and best practices.
- axsaucedo/seldon-core
axsaucedo/seldon-core
Framework for deploying and managing machine learning models.
- googlecloudplatform/mlops-on-gcp
GoogleCloudPlatform/mlops-on-gcp
Guides for implementing MLOps specifically on Google Cloud.
- visenger/handson-ml
visenger/handson-ml
Practical examples for implementing machine learning workflows.
- cmawer/reproducible-model
cmawer/reproducible-model
Guidelines for ensuring model reproducibility in production.
- aporia-ai/mlplatform-workshop
aporia-ai/mlplatform-workshop
Hands-on workshop for building machine learning platforms.
- aronchick/mlops-pipeline
aronchick/MLOps-pipeline
Example implementation of an end-to-end MLOps pipeline.
